KIARA_METADATA

find_model_classes: Union[Type, Tuple, Callable]

Functions

DBG(*objects, *, sep=' ', end='\n', file=None, flush=False)

Source code in kiara/__init__.py

def DBG(
    *objects: typing.Any,
    sep: str = " ",
    end: str = "\n",
    file: typing.Optional[typing.IO[str]] = None,
    flush: bool = False,
):

    objs = (
        ["[green]----------------------------------------------[/green]"]
        + list(objects)
        + ["[green]----------------------------------------------[/green]"]
    )
    dbg(*objs, sep=sep, end=end, file=file, flush=flush)

dbg(*objects, *, sep=' ', end='\n', file=None, flush=False)

Source code in kiara/__init__.py

def dbg(
    *objects: typing.Any,
    sep: str = " ",
    end: str = "\n",
    file: typing.Optional[typing.IO[str]] = None,
    flush: bool = False,
):

    for obj in objects:
        try:
            rich_print(obj, sep=sep, end=end, file=file, flush=flush)
        except Exception:
            rich_print(
                f"[green]{obj}[/green]", sep=sep, end=end, file=file, flush=flush
            )

get_version()

Return the current version of Kiara.

Source code in kiara/__init__.py

def get_version() -> str:
    """Return the current version of *Kiara*."""
    from pkg_resources import DistributionNotFound, get_distribution

    try:
        # Change here if project is renamed and does not equal the package name
        dist_name = __name__
        __version__ = get_distribution(dist_name).version
    except DistributionNotFound:

        try:
            version_file = os.path.join(os.path.dirname(__file__), "version.txt")

            if os.path.exists(version_file):
                with open(version_file, encoding="utf-8") as vf:
                    __version__ = vf.read()
            else:
                __version__ = "unknown"

        except (Exception):
            pass

        if __version__ is None:
            __version__ = "unknown"

    return __version__

Modules

context special

logger

Classes

Kiara

The core context of a kiara session.

The Kiara object holds all information related to the current environment the user does works in. This includes:

• available modules, operations & pipelines
• available value data_types
• available metadata schemas
• available data items
• available controller and processor data_types
• misc. configuration options

It's possible to use kiara without ever manually touching the 'Kiara' class, by default all relevant classes and functions will use a default instance of this class (available via the Kiara.instance() method.

The Kiara class is highly dependent on the Python environment it lives in, because it auto-discovers available sub-classes of its building blocks (modules, value data_types, etc.). So, you can't assume that, for example, a pipeline you create will work the same way (or at all) in a different environment. kiara will always be able to tell you all the details of this environment, though, and it will attach those details to things like data, so there is always a record of how something was created, and in which environment.

Source code in kiara/context/__init__.py

class Kiara(object):
    """The core context of a kiara session.

    The `Kiara` object holds all information related to the current environment the user does works in. This includes:

      - available modules, operations & pipelines
      - available value data_types
      - available metadata schemas
      - available data items
      - available controller and processor data_types
      - misc. configuration options

    It's possible to use *kiara* without ever manually touching the 'Kiara' class, by default all relevant classes and functions
    will use a default instance of this class (available via the `Kiara.instance()` method.

    The Kiara class is highly dependent on the Python environment it lives in, because it auto-discovers available sub-classes
    of its building blocks (modules, value data_types, etc.). So, you can't assume that, for example, a pipeline you create
    will work the same way (or at all) in a different environment. *kiara* will always be able to tell you all the details
    of this environment, though, and it will attach those details to things like data, so there is always a record of
    how something was created, and in which environment.
    """

    _instance = None

    @classmethod
    def instance(cls) -> "Kiara":
        """The default *kiara* context. In most cases, it's recommended you create and manage your own, though."""

        if cls._instance is None:
            cls._instance = Kiara()
        return cls._instance

    def __init__(self, config: Optional[KiaraContextConfig] = None):

        if not config:
            kc = KiaraConfig()
            config = kc.get_context_config()

        self._id: uuid.UUID = ID_REGISTRY.generate(
            id=uuid.UUID(config.context_id), obj=self
        )
        ID_REGISTRY.update_metadata(self._id, kiara_id=self._id)
        self._config: KiaraContextConfig = config

        # if is_debug():
        #     echo = True
        # else:
        #     echo = False
        # self._engine: Engine = create_engine(
        #     self._config.db_url,
        #     echo=echo,
        #     future=True,
        #     json_serializer=orm_json_serialize,
        #     json_deserializer=orm_json_deserialize,
        # )

        # self._run_alembic_migrations()
        # self._envs: Optional[Mapping[str, EnvironmentOrm]] = None

        self._event_registry: EventRegistry = EventRegistry(kiara=self)
        self._type_registry: TypeRegistry = TypeRegistry(self)
        self._data_registry: DataRegistry = DataRegistry(kiara=self)
        self._job_registry: JobRegistry = JobRegistry(kiara=self)
        self._module_registry: ModuleRegistry = ModuleRegistry()
        self._operation_registry: OperationRegistry = OperationRegistry(kiara=self)

        self._kiara_model_registry: ModelRegistry = ModelRegistry.instance()

        self._alias_registry: AliasRegistry = AliasRegistry(kiara=self)
        self._destiny_registry: DestinyRegistry = DestinyRegistry(kiara=self)

        self._env_mgmt: Optional[EnvironmentRegistry] = None

        metadata_augmenter = CreateMetadataDestinies(kiara=self)
        self._event_registry.add_listener(
            metadata_augmenter, *metadata_augmenter.supported_event_types()
        )

        self._context_info: Optional[KiaraContextInfo] = None

        # initialize stores
        self._archive_types = find_all_archive_types()
        self._archives: Dict[str, KiaraArchive] = {}

        for archive_alias, archive in self._config.archives.items():
            archive_cls = self._archive_types.get(archive.archive_type, None)
            if archive_cls is None:
                raise Exception(
                    f"Can't create context: no archive type '{archive.archive_type}' available. Available types: {', '.join(self._archive_types.keys())}"
                )

            config_cls = archive_cls._config_cls
            archive_config = config_cls(**archive.config)
            archive_obj = archive_cls(archive_id=archive.archive_uuid, config=archive_config)  # type: ignore
            for supported_type in archive_obj.supported_item_types():
                if supported_type == "data":
                    self.data_registry.register_data_archive(
                        archive_obj, alias=archive_alias  # type: ignore
                    )
                if supported_type == "job_record":
                    self.job_registry.register_job_archive(archive_obj, alias=archive_alias)  # type: ignore

                if supported_type == "alias":
                    self.alias_registry.register_archive(archive_obj, alias=archive_alias)  # type: ignore

    def _run_alembic_migrations(self):
        script_location = os.path.abspath(KIARA_DB_MIGRATIONS_FOLDER)
        dsn = self._config.db_url
        log_message("running migration script", script=script_location, db_url=dsn)
        from alembic.config import Config

        alembic_cfg = Config(KIARA_DB_MIGRATIONS_CONFIG)
        alembic_cfg.set_main_option("script_location", script_location)
        alembic_cfg.set_main_option("sqlalchemy.url", dsn)
        command.upgrade(alembic_cfg, "head")

    @property
    def id(self) -> uuid.UUID:
        return self._id

    @property
    def context_config(self) -> KiaraContextConfig:
        return self._config

    @property
    def context_info(self) -> "KiaraContextInfo":

        if self._context_info is None:
            self._context_info = KiaraContextInfo.create_from_kiara_instance(kiara=self)
        return self._context_info

    # ===================================================================================================
    # registry accessors

    @property
    def environment_registry(self) -> EnvironmentRegistry:
        if self._env_mgmt is not None:
            return self._env_mgmt

        self._env_mgmt = EnvironmentRegistry.instance()
        return self._env_mgmt

    @property
    def type_registry(self) -> TypeRegistry:
        return self._type_registry

    @property
    def module_registry(self) -> ModuleRegistry:
        return self._module_registry

    @property
    def kiara_model_registry(self) -> ModelRegistry:
        return self._kiara_model_registry

    @property
    def alias_registry(self) -> AliasRegistry:
        return self._alias_registry

    @property
    def destiny_registry(self) -> DestinyRegistry:
        return self._destiny_registry

    @property
    def job_registry(self) -> JobRegistry:
        return self._job_registry

    @property
    def operation_registry(self) -> OperationRegistry:
        op_registry = self._operation_registry
        return op_registry

    @property
    def data_registry(self) -> DataRegistry:
        return self._data_registry

    @property
    def event_registry(self) -> EventRegistry:
        return self._event_registry

    # ===================================================================================================
    # context specific types & instances

    @property
    def current_environments(self) -> Mapping[str, RuntimeEnvironment]:
        return self.environment_registry.environments

    @property
    def data_type_classes(self) -> Mapping[str, Type[DataType]]:
        return self.type_registry.data_type_classes

    @property
    def data_type_names(self) -> List[str]:
        return self.type_registry.data_type_names

    @property
    def module_type_classes(self) -> Mapping[str, Type["KiaraModule"]]:
        return self._module_registry.module_types

    @property
    def module_type_names(self) -> Iterable[str]:
        return self._module_registry.get_module_type_names()

    # ===================================================================================================
    # kiara session API methods

    def create_manifest(
        self, module_or_operation: str, config: Optional[Mapping[str, Any]] = None
    ) -> Manifest:

        if config is None:
            config = {}

        if module_or_operation in self.module_type_names:

            manifest: Manifest = Manifest(
                module_type=module_or_operation, module_config=config
            )

        elif module_or_operation in self.operation_registry.operation_ids:

            if config:
                raise Exception(
                    f"Specified run target '{module_or_operation}' is an operation, additional module configuration is not allowed (yet)."
                )
            manifest = self.operation_registry.get_operation(module_or_operation)

        elif os.path.isfile(module_or_operation):
            raise NotImplementedError()

        else:
            raise Exception(
                f"Can't assemble operation, invalid operation/module name: {module_or_operation}. Must be registered module or operation name, or file."
            )

        return manifest

    def create_module(self, manifest: Union[Manifest, str]) -> "KiaraModule":
        """Create a [KiaraModule][kiara.module.KiaraModule] object from a module configuration.

        Arguments:
            manifest: the module configuration
        """

        return self._module_registry.create_module(manifest=manifest)

    def queue(
        self, manifest: Manifest, inputs: Mapping[str, Any], wait: bool = False
    ) -> uuid.UUID:
        """Queue a job with the specified manifest and inputs.

        Arguments:
           manifest: the job manifest
           inputs: the job inputs
           wait: whether to wait for the job to be finished before returning

        Returns:
            the job id that can be used to look up job status & results
        """

        return self.job_registry.execute(manifest=manifest, inputs=inputs, wait=wait)

    def process(self, manifest: Manifest, inputs: Mapping[str, Any]) -> ValueMap:
        """Queue a job with the specified manifest and inputs.

        Arguments:
           manifest: the job manifest
           inputs: the job inputs
           wait: whether to wait for the job to be finished before returning

        Returns
        """

        return self.job_registry.execute_and_retrieve(manifest=manifest, inputs=inputs)

    def save_values(
        self, values: ValueMap, alias_map: Mapping[str, Iterable[str]]
    ) -> StoreValuesResult:

        _values = {}
        for field_name in values.field_names:
            value = values.get_value_obj(field_name)
            _values[field_name] = value
            self.data_registry.store_value(value=value)
        stored = {}
        for field_name, field_aliases in alias_map.items():

            value = _values[field_name]
            try:
                if field_aliases:
                    self.alias_registry.register_aliases(value.value_id, *field_aliases)

                stored[field_name] = StoreValueResult.construct(
                    value=value, aliases=sorted(field_aliases), error=None
                )

            except Exception as e:
                if is_debug():
                    import traceback

                    traceback.print_exc()
                stored[field_name] = StoreValueResult.construct(
                    value=value, aliases=sorted(field_aliases), error=str(e)
                )

        return StoreValuesResult.construct(__root__=stored)

alias_registry: AliasRegistry property readonly

context_config: KiaraContextConfig property readonly

context_info: KiaraContextInfo property readonly

current_environments: Mapping[str, kiara.models.runtime_environment.RuntimeEnvironment] property readonly

data_registry: DataRegistry property readonly

data_type_classes: Mapping[str, Type[kiara.data_types.DataType]] property readonly

data_type_names: List[str] property readonly

destiny_registry: DestinyRegistry property readonly

environment_registry: EnvironmentRegistry property readonly

event_registry: EventRegistry property readonly

id: UUID property readonly

job_registry: JobRegistry property readonly

kiara_model_registry: ModelRegistry property readonly

module_registry: ModuleRegistry property readonly

module_type_classes: Mapping[str, Type[KiaraModule]] property readonly

module_type_names: Iterable[str] property readonly

operation_registry: OperationRegistry property readonly

type_registry: TypeRegistry property readonly

Methods

create_manifest(self, module_or_operation, config=None)

Source code in kiara/context/__init__.py

def create_manifest(
    self, module_or_operation: str, config: Optional[Mapping[str, Any]] = None
) -> Manifest:

    if config is None:
        config = {}

    if module_or_operation in self.module_type_names:

        manifest: Manifest = Manifest(
            module_type=module_or_operation, module_config=config
        )

    elif module_or_operation in self.operation_registry.operation_ids:

        if config:
            raise Exception(
                f"Specified run target '{module_or_operation}' is an operation, additional module configuration is not allowed (yet)."
            )
        manifest = self.operation_registry.get_operation(module_or_operation)

    elif os.path.isfile(module_or_operation):
        raise NotImplementedError()

    else:
        raise Exception(
            f"Can't assemble operation, invalid operation/module name: {module_or_operation}. Must be registered module or operation name, or file."
        )

    return manifest

create_module(self, manifest)

Create a [KiaraModule][kiara.module.KiaraModule] object from a module configuration.

Parameters:

Name	Type	Description	Default
manifest	Union[kiara.models.module.manifest.Manifest, str]	the module configuration	required

Source code in kiara/context/__init__.py

def create_module(self, manifest: Union[Manifest, str]) -> "KiaraModule":
    """Create a [KiaraModule][kiara.module.KiaraModule] object from a module configuration.

    Arguments:
        manifest: the module configuration
    """

    return self._module_registry.create_module(manifest=manifest)

instance() classmethod

The default kiara context. In most cases, it's recommended you create and manage your own, though.

Source code in kiara/context/__init__.py

@classmethod
def instance(cls) -> "Kiara":
    """The default *kiara* context. In most cases, it's recommended you create and manage your own, though."""

    if cls._instance is None:
        cls._instance = Kiara()
    return cls._instance

process(self, manifest, inputs)

Queue a job with the specified manifest and inputs.

Parameters:

Name	Type	Description	Default
manifest	Manifest	the job manifest	required
inputs	Mapping[str, Any]	the job inputs	required
wait		whether to wait for the job to be finished before returning	required

Returns

Source code in kiara/context/__init__.py

def process(self, manifest: Manifest, inputs: Mapping[str, Any]) -> ValueMap:
    """Queue a job with the specified manifest and inputs.

    Arguments:
       manifest: the job manifest
       inputs: the job inputs
       wait: whether to wait for the job to be finished before returning

    Returns
    """

    return self.job_registry.execute_and_retrieve(manifest=manifest, inputs=inputs)

queue(self, manifest, inputs, wait=False)

Queue a job with the specified manifest and inputs.

Parameters:

Name	Type	Description	Default
manifest	Manifest	the job manifest	required
inputs	Mapping[str, Any]	the job inputs	required
wait	bool	whether to wait for the job to be finished before returning	False

Returns:

Type	Description
UUID	the job id that can be used to look up job status & results

Source code in kiara/context/__init__.py

def queue(
    self, manifest: Manifest, inputs: Mapping[str, Any], wait: bool = False
) -> uuid.UUID:
    """Queue a job with the specified manifest and inputs.

    Arguments:
       manifest: the job manifest
       inputs: the job inputs
       wait: whether to wait for the job to be finished before returning

    Returns:
        the job id that can be used to look up job status & results
    """

    return self.job_registry.execute(manifest=manifest, inputs=inputs, wait=wait)

save_values(self, values, alias_map)

Source code in kiara/context/__init__.py

def save_values(
    self, values: ValueMap, alias_map: Mapping[str, Iterable[str]]
) -> StoreValuesResult:

    _values = {}
    for field_name in values.field_names:
        value = values.get_value_obj(field_name)
        _values[field_name] = value
        self.data_registry.store_value(value=value)
    stored = {}
    for field_name, field_aliases in alias_map.items():

        value = _values[field_name]
        try:
            if field_aliases:
                self.alias_registry.register_aliases(value.value_id, *field_aliases)

            stored[field_name] = StoreValueResult.construct(
                value=value, aliases=sorted(field_aliases), error=None
            )

        except Exception as e:
            if is_debug():
                import traceback

                traceback.print_exc()
            stored[field_name] = StoreValueResult.construct(
                value=value, aliases=sorted(field_aliases), error=str(e)
            )

    return StoreValuesResult.construct(__root__=stored)

KiaraContextInfo (KiaraModel) pydantic-model

Source code in kiara/context/__init__.py

class KiaraContextInfo(KiaraModel):
    @classmethod
    def create_from_kiara_instance(
        cls, kiara: "Kiara", package_filter: Optional[str] = None
    ):

        data_types = kiara.type_registry.get_context_metadata(
            only_for_package=package_filter
        )
        modules = kiara.module_registry.get_context_metadata(
            only_for_package=package_filter
        )
        operation_types = kiara.operation_registry.get_context_metadata(
            only_for_package=package_filter
        )
        operations = filter_operations(
            kiara=kiara, pkg_name=package_filter, **kiara.operation_registry.operations
        )

        model_registry = kiara.kiara_model_registry
        if package_filter:
            kiara_models = model_registry.get_models_for_package(
                package_name=package_filter
            )
        else:
            kiara_models = model_registry.all_models

        # metadata_types = find_metadata_models(only_for_package=package_filter)

        return KiaraContextInfo.construct(
            kiara_id=kiara.id,
            package_filter=package_filter,
            data_types=data_types,
            module_types=modules,
            kiara_model_types=kiara_models,
            # metadata_types=metadata_types,
            operation_types=operation_types,
            operations=operations,
        )

    kiara_id: uuid.UUID = Field(description="The id of the kiara context.")
    package_filter: Optional[str] = Field(
        description="Whether this context is filtered to only include information included in a specific Python package."
    )
    data_types: DataTypeClassesInfo = Field(description="The included data types.")
    module_types: ModuleTypeClassesInfo = Field(
        description="The included kiara module types."
    )
    kiara_model_types: KiaraModelClassesInfo = Field(
        description="The included model classes."
    )
    # metadata_types: MetadataTypeClassesInfo = Field(
    #     description="The included value metadata types."
    # )
    operation_types: OperationTypeClassesInfo = Field(
        description="The included operation types."
    )
    operations: OperationGroupInfo = Field(description="The included operations.")

    def _retrieve_id(self) -> str:
        if not self.package_filter:
            return str(self.kiara_id)
        else:
            return f"{self.kiara_id}.package_{self.package_filter}"

    def _retrieve_data_to_hash(self) -> Any:
        return {"kiara_id": self.kiara_id, "package": self.package_filter}

    def get_info(self, item_type: str, item_id: str) -> ItemInfo:

        if "data_type" == item_type or "data_types" == item_type:
            group_info: InfoModelGroup = self.data_types
        elif "module" in item_type:
            group_info = self.module_types
        # elif "metadata" in item_type:
        #     group_info = self.metadata_types
        elif "operation_type" in item_type or "operation_types" in item_type:
            group_info = self.operation_types
        elif "operation" in item_type:
            group_info = self.operations
        elif "kiara_model" in item_type:
            group_info = self.kiara_model_types
        else:
            item_types = [
                "data_type",
                "module_type",
                "kiara_model_type",
                "operation_type",
                "operation",
            ]
            raise Exception(
                f"Can't determine item type '{item_type}', use one of: {', '.join(item_types)}"
            )
        return group_info[item_id]

    def get_all_info(self, skip_empty_types: bool = True) -> Dict[str, InfoModelGroup]:

        result: Dict[str, InfoModelGroup] = {}
        if self.data_types or not skip_empty_types:
            result["data_types"] = self.data_types
        if self.module_types or not skip_empty_types:
            result["module_types"] = self.module_types
        if self.kiara_model_types or not skip_empty_types:
            result["kiara_model_types"] = self.kiara_model_types
        # if self.metadata_types or not skip_empty_types:
        #     result["metadata_types"] = self.metadata_types
        if self.operation_types or not skip_empty_types:
            result["operation_types"] = self.operation_types
        if self.operations or not skip_empty_types:
            result["operations"] = self.operations

        return result

Attributes

data_types: DataTypeClassesInfo pydantic-field required

The included data types.

kiara_id: UUID pydantic-field required

The id of the kiara context.

kiara_model_types: KiaraModelClassesInfo pydantic-field required

The included model classes.

module_types: ModuleTypeClassesInfo pydantic-field required

The included kiara module types.

operation_types: OperationTypeClassesInfo pydantic-field required

The included operation types.

operations: OperationGroupInfo pydantic-field required

The included operations.

package_filter: str pydantic-field

Whether this context is filtered to only include information included in a specific Python package.

create_from_kiara_instance(kiara, package_filter=None) classmethod

Source code in kiara/context/__init__.py

@classmethod
def create_from_kiara_instance(
    cls, kiara: "Kiara", package_filter: Optional[str] = None
):

    data_types = kiara.type_registry.get_context_metadata(
        only_for_package=package_filter
    )
    modules = kiara.module_registry.get_context_metadata(
        only_for_package=package_filter
    )
    operation_types = kiara.operation_registry.get_context_metadata(
        only_for_package=package_filter
    )
    operations = filter_operations(
        kiara=kiara, pkg_name=package_filter, **kiara.operation_registry.operations
    )

    model_registry = kiara.kiara_model_registry
    if package_filter:
        kiara_models = model_registry.get_models_for_package(
            package_name=package_filter
        )
    else:
        kiara_models = model_registry.all_models

    # metadata_types = find_metadata_models(only_for_package=package_filter)

    return KiaraContextInfo.construct(
        kiara_id=kiara.id,
        package_filter=package_filter,
        data_types=data_types,
        module_types=modules,
        kiara_model_types=kiara_models,
        # metadata_types=metadata_types,
        operation_types=operation_types,
        operations=operations,
    )

get_all_info(self, skip_empty_types=True)

Source code in kiara/context/__init__.py

def get_all_info(self, skip_empty_types: bool = True) -> Dict[str, InfoModelGroup]:

    result: Dict[str, InfoModelGroup] = {}
    if self.data_types or not skip_empty_types:
        result["data_types"] = self.data_types
    if self.module_types or not skip_empty_types:
        result["module_types"] = self.module_types
    if self.kiara_model_types or not skip_empty_types:
        result["kiara_model_types"] = self.kiara_model_types
    # if self.metadata_types or not skip_empty_types:
    #     result["metadata_types"] = self.metadata_types
    if self.operation_types or not skip_empty_types:
        result["operation_types"] = self.operation_types
    if self.operations or not skip_empty_types:
        result["operations"] = self.operations

    return result

get_info(self, item_type, item_id)

Source code in kiara/context/__init__.py

def get_info(self, item_type: str, item_id: str) -> ItemInfo:

    if "data_type" == item_type or "data_types" == item_type:
        group_info: InfoModelGroup = self.data_types
    elif "module" in item_type:
        group_info = self.module_types
    # elif "metadata" in item_type:
    #     group_info = self.metadata_types
    elif "operation_type" in item_type or "operation_types" in item_type:
        group_info = self.operation_types
    elif "operation" in item_type:
        group_info = self.operations
    elif "kiara_model" in item_type:
        group_info = self.kiara_model_types
    else:
        item_types = [
            "data_type",
            "module_type",
            "kiara_model_type",
            "operation_type",
            "operation",
        ]
        raise Exception(
            f"Can't determine item type '{item_type}', use one of: {', '.join(item_types)}"
        )
    return group_info[item_id]

Functions

explain(item)

Pretty print information about an item on the terminal.

Source code in kiara/context/__init__.py

def explain(item: Any):
    """Pretty print information about an item on the terminal."""

    if isinstance(item, type):
        from kiara.modules import KiaraModule

        if issubclass(item, KiaraModule):
            item = KiaraModuleTypeInfo.create_from_type_class(type_cls=item)

    console = get_console()
    console.print(item)

Modules

config

yaml

Classes

KiaraArchiveConfig (BaseModel) pydantic-model

Source code in kiara/context/config.py

class KiaraArchiveConfig(BaseModel):

    archive_id: str = Field(description="The unique archive id.")
    archive_type: str = Field(description="The archive type.")
    config: Mapping[str, Any] = Field(
        description="Archive type specific config.", default_factory=dict
    )

    @property
    def archive_uuid(self) -> uuid.UUID:
        return uuid.UUID(self.archive_id)

Attributes

archive_id: str pydantic-field required

The unique archive id.

archive_type: str pydantic-field required

The archive type.

archive_uuid: UUID property readonly

config: Mapping[str, Any] pydantic-field

Archive type specific config.

KiaraConfig (BaseSettings) pydantic-model

Source code in kiara/context/config.py

class KiaraConfig(BaseSettings):
    class Config:
        env_prefix = "kiara_"
        extra = Extra.forbid

        # @classmethod
        # def customise_sources(
        #     cls,
        #     init_settings,
        #     env_settings,
        #     file_secret_settings,
        # ):
        #     return (init_settings, env_settings, config_file_settings_source)

    @classmethod
    def create_in_folder(cls, path: Union[Path, str]) -> "KiaraConfig":

        if isinstance(path, str):
            path = Path(path)
        path = path.absolute()
        if path.exists():
            raise Exception(
                f"Can't create new kiara config, path exists: {path.as_posix()}"
            )

        config = KiaraConfig(base_data_path=path)
        config_file = path / KIARA_CONFIG_FILE_NAME

        config.save(config_file)

        return config

    @classmethod
    def load_from_file(cls, path: Optional[Path] = None) -> "KiaraConfig":

        if path is None:
            path = Path(KIARA_MAIN_CONFIG_FILE)

        if not path.exists():
            raise Exception(
                f"Can't load kiara config, path does not exist: {path.as_posix()}"
            )

        if path.is_dir():
            path = path / KIARA_CONFIG_FILE_NAME
            if not path.exists():
                raise Exception(
                    f"Can't load kiara config, path does not exist: {path.as_posix()}"
                )

        with path.open("rt") as f:
            data = yaml.load(f)

        return KiaraConfig(**data)

    context_search_paths: List[str] = Field(
        description="The base path to look for contexts in.",
        default=[KIARA_MAIN_CONTEXTS_PATH],
    )
    base_data_path: str = Field(
        description="The base path to use for all data (unless otherwise specified.",
        default=kiara_app_dirs.user_data_dir,
    )
    stores_base_path: str = Field(
        description="The base path for the stores of this context."
    )
    default_context: str = Field(
        description="The name of the default context to use if none is provided.",
        default=DEFAULT_CONTEXT_NAME,
    )
    auto_generate_contexts: bool = Field(
        description="Whether to auto-generate requested contexts if they don't exist yet.",
        default=True,
    )
    _contexts: Dict[uuid.UUID, "Kiara"] = PrivateAttr(default_factory=dict)
    _available_context_files: Dict[str, Path] = PrivateAttr(default=None)
    _context_data: Dict[str, KiaraContextConfig] = PrivateAttr(default_factory=dict)
    _config_path: Optional[Path] = PrivateAttr(default=None)

    @validator("context_search_paths")
    def validate_context_search_paths(cls, v):

        if not v or not v[0]:
            v = [KIARA_MAIN_CONTEXTS_PATH]

        return v

    @root_validator(pre=True)
    def _set_paths(cls, values: Any):

        base_path = values.get("base_data_path", None)
        if not base_path:
            base_path = os.path.abspath(kiara_app_dirs.user_data_dir)
            values["base_data_path"] = base_path
        elif isinstance(base_path, Path):
            base_path = base_path.absolute().as_posix()
            values["base_data_path"] = base_path

        stores_base_path = values.get("stores_base_path", None)
        if not stores_base_path:
            stores_base_path = os.path.join(base_path, "stores")
            values["stores_base_path"] = stores_base_path

        context_search_paths = values.get("context_search_paths")
        if not context_search_paths:
            context_search_paths = [os.path.join(base_path, "contexts")]
            values["context_search_paths"] = context_search_paths

        return values

    @property
    def available_context_names(self) -> Iterable[str]:

        if self._available_context_files is not None:
            return self._available_context_files.keys()

        result = {}
        for search_path in self.context_search_paths:
            sp = Path(search_path)
            for path in sp.rglob("*.yaml"):
                rel_path = path.relative_to(sp)
                alias = rel_path.as_posix()[0:-5]
                alias = alias.replace(os.sep, ".")
                result[alias] = path
        self._available_context_files = result
        return self._available_context_files.keys()

    def get_context_config(
        self, context_alias: str = DEFAULT_CONTEXT_NAME
    ) -> KiaraContextConfig:

        if context_alias not in self.available_context_names:
            if (
                not self.auto_generate_contexts
                and not context_alias == DEFAULT_CONTEXT_NAME
            ):
                raise Exception(
                    f"No kiara context with name '{context_alias}' available."
                )
            else:
                return self.create_context_config(context_alias=context_alias)

        if context_alias in self._context_data.keys():
            return self._context_data[context_alias]

        context_file = self._available_context_files[context_alias]
        context_data = get_data_from_file(context_file, content_type="yaml")

        context = KiaraContextConfig(**context_data)
        context._context_config_path = context_file

        self._context_data[context_alias] = context
        return context

    def create_context_config(
        self, context_alias: Optional[str] = None
    ) -> KiaraContextConfig:

        if not context_alias:
            context_alias = DEFAULT_CONTEXT_NAME
        if context_alias in self.available_context_names:
            raise Exception(
                f"Can't create kiara context '{context_alias}': context with that alias already registered."
            )

        if os.path.sep in context_alias:
            raise Exception(
                f"Can't create context with alias '{context_alias}': no special characters allowed."
            )

        context_file = (
            Path(os.path.join(self.context_search_paths[0])) / f"{context_alias}.yaml"
        )

        archives = create_default_archives(kiara_config=self)
        context_id = ID_REGISTRY.generate(
            obj_type=KiaraContextConfig, comment=f"new kiara context '{context_alias}'"
        )

        context_config = KiaraContextConfig(
            context_id=str(context_id), archives=archives, extra_pipeline_folders=[]
        )

        context_file.parent.mkdir(parents=True, exist_ok=True)
        with open(context_file, "wt") as f:
            yaml.dump(context_config.dict(), f)

        context_config._context_config_path = context_file

        self._available_context_files[context_alias] = context_file
        self._context_data[context_alias] = context_config

        return context_config

    def create_context(
        self, context: Union[None, str, uuid.UUID, Path] = None
    ) -> "Kiara":

        if not context:
            context = DEFAULT_CONTEXT_NAME
        else:
            try:
                context = uuid.UUID(context)  # type: ignore
            except Exception:
                pass

        if isinstance(context, str) and os.path.exists(context):
            context = Path(context)

        if isinstance(context, Path):
            with context.open("rt") as f:
                data = yaml.load(f)
            context_config = KiaraContextConfig(**data)
        elif isinstance(context, str):
            context_config = self.get_context_config(context_alias=context)
        elif isinstance(context, uuid.UUID):
            context_config = self.find_context_config(context_id=context)
        else:
            raise Exception(
                f"Can't retrieve context, invalid context config type '{type(context)}'."
            )

        assert context_config.context_id not in self._contexts.keys()

        from kiara.context import Kiara

        kiara = Kiara(config=context_config)
        assert kiara.id == uuid.UUID(context_config.context_id)
        self._contexts[kiara.id] = kiara

        return kiara

    def find_context_config(self, context_id: uuid.UUID) -> KiaraContextConfig:
        raise NotImplementedError()

    def save(self, path: Optional[Path] = None):
        if path is None:
            path = Path(KIARA_MAIN_CONFIG_FILE)

        if path.exists():
            raise Exception(
                f"Can't save config file, path already exists: {path.as_posix()}"
            )

        path.parent.mkdir(parents=True, exist_ok=True)

        with path.open("wt") as f:
            yaml.dump(self.dict(exclude={"context"}), f)

        self._config_path = path

Attributes

auto_generate_contexts: bool pydantic-field

Whether to auto-generate requested contexts if they don't exist yet.

available_context_names: Iterable[str] property readonly

base_data_path: str pydantic-field

The base path to use for all data (unless otherwise specified.

context_search_paths: List[str] pydantic-field

The base path to look for contexts in.

default_context: str pydantic-field

The name of the default context to use if none is provided.

stores_base_path: str pydantic-field required

The base path for the stores of this context.

Config

Source code in kiara/context/config.py

class Config:
    env_prefix = "kiara_"
    extra = Extra.forbid

    # @classmethod
    # def customise_sources(
    #     cls,
    #     init_settings,
    #     env_settings,
    #     file_secret_settings,
    # ):
    #     return (init_settings, env_settings, config_file_settings_source)

env_prefix

extra

create_context(self, context=None)

Source code in kiara/context/config.py

def create_context(
    self, context: Union[None, str, uuid.UUID, Path] = None
) -> "Kiara":

    if not context:
        context = DEFAULT_CONTEXT_NAME
    else:
        try:
            context = uuid.UUID(context)  # type: ignore
        except Exception:
            pass

    if isinstance(context, str) and os.path.exists(context):
        context = Path(context)

    if isinstance(context, Path):
        with context.open("rt") as f:
            data = yaml.load(f)
        context_config = KiaraContextConfig(**data)
    elif isinstance(context, str):
        context_config = self.get_context_config(context_alias=context)
    elif isinstance(context, uuid.UUID):
        context_config = self.find_context_config(context_id=context)
    else:
        raise Exception(
            f"Can't retrieve context, invalid context config type '{type(context)}'."
        )

    assert context_config.context_id not in self._contexts.keys()

    from kiara.context import Kiara

    kiara = Kiara(config=context_config)
    assert kiara.id == uuid.UUID(context_config.context_id)
    self._contexts[kiara.id] = kiara

    return kiara

create_context_config(self, context_alias=None)

Source code in kiara/context/config.py

def create_context_config(
    self, context_alias: Optional[str] = None
) -> KiaraContextConfig:

    if not context_alias:
        context_alias = DEFAULT_CONTEXT_NAME
    if context_alias in self.available_context_names:
        raise Exception(
            f"Can't create kiara context '{context_alias}': context with that alias already registered."
        )

    if os.path.sep in context_alias:
        raise Exception(
            f"Can't create context with alias '{context_alias}': no special characters allowed."
        )

    context_file = (
        Path(os.path.join(self.context_search_paths[0])) / f"{context_alias}.yaml"
    )

    archives = create_default_archives(kiara_config=self)
    context_id = ID_REGISTRY.generate(
        obj_type=KiaraContextConfig, comment=f"new kiara context '{context_alias}'"
    )

    context_config = KiaraContextConfig(
        context_id=str(context_id), archives=archives, extra_pipeline_folders=[]
    )

    context_file.parent.mkdir(parents=True, exist_ok=True)
    with open(context_file, "wt") as f:
        yaml.dump(context_config.dict(), f)

    context_config._context_config_path = context_file

    self._available_context_files[context_alias] = context_file
    self._context_data[context_alias] = context_config

    return context_config

create_in_folder(path) classmethod

Source code in kiara/context/config.py

@classmethod
def create_in_folder(cls, path: Union[Path, str]) -> "KiaraConfig":

    if isinstance(path, str):
        path = Path(path)
    path = path.absolute()
    if path.exists():
        raise Exception(
            f"Can't create new kiara config, path exists: {path.as_posix()}"
        )

    config = KiaraConfig(base_data_path=path)
    config_file = path / KIARA_CONFIG_FILE_NAME

    config.save(config_file)

    return config

find_context_config(self, context_id)

Source code in kiara/context/config.py

def find_context_config(self, context_id: uuid.UUID) -> KiaraContextConfig:
    raise NotImplementedError()

get_context_config(self, context_alias='default')

Source code in kiara/context/config.py

def get_context_config(
    self, context_alias: str = DEFAULT_CONTEXT_NAME
) -> KiaraContextConfig:

    if context_alias not in self.available_context_names:
        if (
            not self.auto_generate_contexts
            and not context_alias == DEFAULT_CONTEXT_NAME
        ):
            raise Exception(
                f"No kiara context with name '{context_alias}' available."
            )
        else:
            return self.create_context_config(context_alias=context_alias)

    if context_alias in self._context_data.keys():
        return self._context_data[context_alias]

    context_file = self._available_context_files[context_alias]
    context_data = get_data_from_file(context_file, content_type="yaml")

    context = KiaraContextConfig(**context_data)
    context._context_config_path = context_file

    self._context_data[context_alias] = context
    return context

load_from_file(path=None) classmethod

Source code in kiara/context/config.py

@classmethod
def load_from_file(cls, path: Optional[Path] = None) -> "KiaraConfig":

    if path is None:
        path = Path(KIARA_MAIN_CONFIG_FILE)

    if not path.exists():
        raise Exception(
            f"Can't load kiara config, path does not exist: {path.as_posix()}"
        )

    if path.is_dir():
        path = path / KIARA_CONFIG_FILE_NAME
        if not path.exists():
            raise Exception(
                f"Can't load kiara config, path does not exist: {path.as_posix()}"
            )

    with path.open("rt") as f:
        data = yaml.load(f)

    return KiaraConfig(**data)

save(self, path=None)

Source code in kiara/context/config.py

def save(self, path: Optional[Path] = None):
    if path is None:
        path = Path(KIARA_MAIN_CONFIG_FILE)

    if path.exists():
        raise Exception(
            f"Can't save config file, path already exists: {path.as_posix()}"
        )

    path.parent.mkdir(parents=True, exist_ok=True)

    with path.open("wt") as f:
        yaml.dump(self.dict(exclude={"context"}), f)

    self._config_path = path

validate_context_search_paths(v) classmethod

Source code in kiara/context/config.py

@validator("context_search_paths")
def validate_context_search_paths(cls, v):

    if not v or not v[0]:
        v = [KIARA_MAIN_CONTEXTS_PATH]

    return v

KiaraContextConfig (BaseSettings) pydantic-model

Source code in kiara/context/config.py

class KiaraContextConfig(BaseSettings):
    class Config:
        extra = Extra.forbid

    context_id: str = Field(description="A globally unique id for this kiara context.")

    # context_alias: str = Field(
    #     description="An alias for this kiara context, must be unique within all known contexts."
    # )
    # context_folder: str = Field(
    #     description="The base folder where settings and data for this kiara context will be stored."
    # )

    archives: Dict[str, KiaraArchiveConfig] = Field(
        description="All the archives this kiara context can use and the aliases they are registered with."
    )
    extra_pipeline_folders: List[str] = Field(
        description="Paths to local folders that contain kiara pipelines.",
        default_factory=list,
    )
    _context_config_path: Optional[Path] = PrivateAttr(default=None)

    # ignore_errors: bool = Field(
    #     description="If set, kiara will try to ignore most errors (that can be ignored).",
    #     default=False,
    # )

    # @property
    # def db_url(self):
    #     return get_kiara_db_url(self.context_folder)
    #
    # @property
    # def data_directory(self) -> str:
    #     return os.path.join(self.context_folder, "data")

Attributes

archives: Dict[str, kiara.context.config.KiaraArchiveConfig] pydantic-field required

All the archives this kiara context can use and the aliases they are registered with.

context_id: str pydantic-field required

A globally unique id for this kiara context.

extra_pipeline_folders: List[str] pydantic-field

Paths to local folders that contain kiara pipelines.

Config

Source code in kiara/context/config.py

class Config:
    extra = Extra.forbid

config_file_settings_source(settings)

Source code in kiara/context/config.py

def config_file_settings_source(settings: BaseSettings) -> Dict[str, Any]:
    if os.path.isfile(KIARA_MAIN_CONFIG_FILE):
        config = get_data_from_file(KIARA_MAIN_CONFIG_FILE, content_type="yaml")
        if not isinstance(config, Mapping):
            raise ValueError(
                f"Invalid config file format, can't parse file: {KIARA_MAIN_CONFIG_FILE}"
            )
    else:
        config = {}
    return config

create_default_archives(kiara_config)

Source code in kiara/context/config.py

def create_default_archives(kiara_config: "KiaraConfig"):

    env_registry = EnvironmentRegistry.instance()

    archives = env_registry.environments["kiara_types"].archive_types
    data_store_type = "filesystem_data_store"

    assert data_store_type in archives.keys()

    data_store_id = ID_REGISTRY.generate(comment="default data store id")
    data_archive_config = {
        "archive_path": os.path.abspath(
            os.path.join(
                kiara_config.stores_base_path, data_store_type, str(data_store_id)
            )
        )
    }
    data_store = KiaraArchiveConfig.construct(
        archive_id=str(data_store_id),
        archive_type=data_store_type,
        config=data_archive_config,
    )

    job_store_type = "filesystem_job_store"
    assert job_store_type in archives.keys()
    job_store_id = ID_REGISTRY.generate(comment="default job store id")
    job_archive_config = {
        "archive_path": os.path.abspath(
            os.path.join(
                kiara_config.stores_base_path, job_store_type, str(job_store_id)
            )
        )
    }
    job_store = KiaraArchiveConfig.construct(
        archive_id=str(job_store_id),
        archive_type=job_store_type,
        config=job_archive_config,
    )

    alias_store_type = "filesystem_alias_store"
    assert job_store_type in archives.keys()
    alias_store_id = ID_REGISTRY.generate(comment="default alias store id")
    alias_store_config = {
        "archive_path": os.path.abspath(
            os.path.join(
                kiara_config.stores_base_path, alias_store_type, str(alias_store_id)
            )
        )
    }
    alias_store = KiaraArchiveConfig.construct(
        archive_id=str(alias_store_id),
        archive_type=alias_store_type,
        config=alias_store_config,
    )

    return {
        DEFAULT_DATA_STORE_MARKER: data_store,
        DEFAULT_JOB_STORE_MARKER: job_store,
        DEFAULT_ALIAS_STORE_MARKER: alias_store,
    }

orm

Base: DeclarativeMeta

jobs_env_association_table

value_env_association_table

AliasOrm (Base)

Source code in kiara/context/orm.py

class AliasOrm(Base):

    __tablename__ = "aliases"

    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    alias: Column[str] = Column(String, index=True, nullable=False)
    created: Column[datetime] = Column(UtcDateTime(), nullable=False, index=True)
    version: Column[int] = Column(Integer, nullable=False, index=True)
    value_id: Column[Optional[uuid.UUID]] = Column(UUIDType(binary=True), nullable=True)

    UniqueConstraint(alias, version)

alias: Column

created: Column

id: Column

value_id: Column

version: Column

DestinyOrm (Base)

Source code in kiara/context/orm.py

class DestinyOrm(Base):
    __tablename__ = "destinies"

    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    value_id: Column[int] = Column(Integer, ForeignKey("values.id"), nullable=False)
    category: Column[str] = Column(String, nullable=False, index=False)
    key: Column[str] = Column(String, nullable=False, index=False)
    manifest_id: Column[int] = Column(
        Integer, ForeignKey("manifests.id"), nullable=False
    )
    inputs: Column[Union[Dict[Any, Any], List[Any]]] = Column(
        JSON, index=False, nullable=False
    )
    output_name: Column[str] = Column(String, index=False, nullable=False)
    destiny_value: Column[Optional[int]] = Column(
        Integer, ForeignKey("values.id"), nullable=True
    )
    description: Column[Optional[str]] = Column(String, nullable=True)

    UniqueConstraint(value_id, category, key)

category: Column

description: Column

destiny_value: Column

id: Column

inputs: Column

key: Column

manifest_id: Column

output_name: Column

value_id: Column

EnvironmentOrm (Base)

Source code in kiara/context/orm.py

class EnvironmentOrm(Base):
    __tablename__ = "environments"

    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    metadata_hash: Column[int] = Column(Integer, index=True, nullable=False)
    metadata_schema_id = Column(
        Integer, ForeignKey("metadata_schema_lookup.id"), nullable=False
    )
    metadata_payload: Column[Union[Dict[Any, Any], List[Any]]] = Column(
        JSON, nullable=False
    )

    UniqueConstraint(metadata_hash)

id: Column

metadata_hash: Column

metadata_payload: Column

metadata_schema_id

JobsOrm (Base)

Source code in kiara/context/orm.py

class JobsOrm(Base):

    __tablename__ = "jobs"
    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    manifest_id: Column[int] = Column(
        Integer, ForeignKey("manifests.id"), nullable=False
    )
    inputs: Column[Union[Dict[Any, Any], List[Any]]] = Column(JSON, nullable=False)
    input_hash: Column[str] = Column(String, nullable=False)
    is_idempotent: Column[bool] = Column(Boolean, nullable=False)
    created: Column[datetime] = Column(UtcDateTime(), default=utcnow(), nullable=False)
    started: Column[Optional[datetime]] = Column(UtcDateTime(), nullable=True)
    duration_ms: Column[Optional[int]] = Column(Integer, nullable=True)
    environments = relationship("EnvironmentOrm", secondary=jobs_env_association_table)

created: Column

duration_ms: Column

environments

id: Column

input_hash: Column

inputs: Column

is_idempotent: Column

manifest_id: Column

started: Column

ManifestOrm (Base)

Source code in kiara/context/orm.py

class ManifestOrm(Base):
    __tablename__ = "manifests"

    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    module_type: Column[str] = Column(String, index=True, nullable=False)
    module_config: Column[Union[Dict[Any, Any], List[Any]]] = Column(
        JSON, nullable=False
    )
    manifest_hash: Column[int] = Column(Integer, index=True, nullable=False)
    is_idempotent: Column[bool] = Column(Boolean, nullable=False)

    UniqueConstraint(module_type, manifest_hash)

id: Column

is_idempotent: Column

manifest_hash: Column

module_config: Column

module_type: Column

MetadataSchemaOrm (Base)

Source code in kiara/context/orm.py

class MetadataSchemaOrm(Base):
    __tablename__ = "metadata_schema_lookup"

    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    metadata_schema_hash: Column[int] = Column(Integer, index=True, nullable=False)
    metadata_type: Column[str] = Column(String, nullable=False)
    metadata_schema: Column[Union[Dict[Any, Any], List[Any]]] = Column(
        JSON, nullable=False
    )
    metadata_payloads = relationship("EnvironmentOrm")

    UniqueConstraint(metadata_schema_hash)

id: Column

metadata_payloads

metadata_schema: Column

metadata_schema_hash: Column

metadata_type: Column

Pedigree (Base)

Source code in kiara/context/orm.py

class Pedigree(Base):
    __tablename__ = "pedigrees"

    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    manifest_id: Column[int] = Column(
        Integer, ForeignKey("manifests.id"), nullable=False
    )
    inputs: Column[Union[Dict[Any, Any], List[Any]]] = Column(JSON, nullable=False)

id: Column

inputs: Column

manifest_id: Column

ValueOrm (Base)

Source code in kiara/context/orm.py

class ValueOrm(Base):
    __tablename__ = "values"

    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    global_id: Column[uuid.UUID] = Column(UUIDType(binary=True), nullable=False)
    data_type_id: Column[int] = Column(
        Integer, ForeignKey("data_types.id"), nullable=False
    )
    data_type_name: Column[str] = Column(String, index=True, nullable=False)
    value_size: Column[int] = Column(Integer, index=True, nullable=False)
    value_hash: Column[str] = Column(String, index=True, nullable=False)
    environments = relationship("EnvironmentOrm", secondary=value_env_association_table)

    UniqueConstraint(value_hash, value_size, data_type_id)

data_type_id: Column

data_type_name: Column

environments

global_id: Column

id: Column

value_hash: Column

value_size: Column

ValueTypeOrm (Base)

Source code in kiara/context/orm.py

class ValueTypeOrm(Base):
    __tablename__ = "data_types"

    id: Column[Optional[int]] = Column(Integer, primary_key=True)
    type_config_hash: Column[int] = Column(Integer, index=True, nullable=False)
    type_name: Column[str] = Column(String, nullable=False, index=True)
    type_config: Column[Union[Dict[Any, Any], List[Any]]] = Column(JSON, nullable=False)

    UniqueConstraint(type_config_hash, type_name)

id: Column

type_config: Column

type_config_hash: Column

type_name: Column

data_types special

This is the base module that contains everything data type-related in kiara.

I'm still not 100% sure how to best implement the kiara type system, there are several ways it could be done, for example based on Python type-hints, using JSON-schema, Avro (which is my 2nd favourite option), as well as by implementing a custom type-class hierarchy. Which is what I have choosen to try first. For now, it looks like it'll work out, but there is a chance requirements I haven't forseen will crop up that could make this become ugly.

Anyway, the way it works (for now) is that kiara comes with a set of often used data_types (the standard set of: scalars, list, dict, table & array, etc.) which each come with 2 functions that can serialize and deserialize values of that type in a persistant fashion -- which could be storing as a file on disk, or as a cell/row in a database. Those functions will most likley be kiara modules themselves, with even more restricted input/output type options.

In addition, packages that contain modules can implement their own, custom data_types, if suitable ones are not available in core-kiara. Those can either be 'serialized/deserialized' into kiara-native data_types (which in turn will serialize them using their own serializing functions), or will have to implement custom serializing functionality (which will probably be discouraged, since this might not be trivial and there are quite a few things to consider).

TYPE_CONFIG_CLS

TYPE_PYTHON_CLS

logger

Classes

DataType (ABC, Generic)

Base class that all kiara data_types must inherit from.

kiara data_types have 3 main responsibilities:

• serialize into / deserialize from persistent state
• data validation
• metadata extraction

Serializing being the arguably most important of those, because without most of the data management features of kiara would be impossible. Validation should not require any explanation. Metadata extraction is important, because that metadata will be available to other components of kiara (or frontends for it), without them having to request the actual data. That will hopefully make kiara very efficient in terms of memory management, as well as data transfer and I/O. Ideally, the actual data (bytes) will only be requested at the last possible moment. For example when a module needs the input data to do processing on it -- and even then it might be that it only requests a part of the data, say a single column of a table. Or when a frontend needs to display/visualize the data.

Source code in kiara/data_types/__init__.py

class DataType(abc.ABC, Generic[TYPE_PYTHON_CLS, TYPE_CONFIG_CLS]):
    """Base class that all *kiara* data_types must inherit from.

    *kiara* data_types have 3 main responsibilities:

     - serialize into / deserialize from persistent state
     - data validation
     - metadata extraction

     Serializing being the arguably most important of those, because without most of the data management features of
     *kiara* would be impossible. Validation should not require any explanation. Metadata extraction is important, because
     that metadata will be available to other components of *kiara* (or frontends for it), without them having to request
     the actual data. That will hopefully make *kiara* very efficient in terms of memory management, as well as data
     transfer and I/O. Ideally, the actual data (bytes) will only be requested at the last possible moment. For example when a
     module needs the input data to do processing on it -- and even then it might be that it only requests a part of the
     data, say a single column of a table. Or when a frontend needs to display/visualize the data.
    """

    @classmethod
    def retrieve_available_type_profiles(cls) -> Mapping[str, Mapping[str, Any]]:
        return {}

    @classmethod
    @abc.abstractmethod
    def python_class(cls) -> Type[TYPE_PYTHON_CLS]:
        pass

    @classmethod
    def data_type_config_class(cls) -> Type[TYPE_CONFIG_CLS]:
        return DataTypeConfig  # type: ignore

    @classmethod
    def _calculate_data_type_hash(
        cls, data_type_config: Union[Mapping[str, Any], DataTypeConfig]
    ) -> int:

        if isinstance(data_type_config, Mapping):
            data_type_config = cls.data_type_config_class()(**data_type_config)  # type: ignore

        obj = {
            "type": cls._data_type_name,  # type: ignore
            "type_config": data_type_config.config_hash,
        }
        h = DeepHash(obj, hasher=KIARA_HASH_FUNCTION)
        return h[obj]

    def __init__(self, **type_config: Any):

        try:
            self._type_config: TYPE_CONFIG_CLS = self.__class__.data_type_config_class()(**type_config)  # type: ignore  # TODO: double-check this is only a mypy issue
        except ValidationError as ve:
            raise ValueTypeConfigException(
                f"Error creating object for type: {ve}",
                self.__class__,
                type_config,
                ve,
            )

        self._data_type_hash: Optional[int] = None
        self._characteristics: Optional[DataTypeCharacteristics] = None

    @property
    def data_type_name(self) -> str:
        return self._data_type_name  # type: ignore

    @property
    def data_type_hash(self) -> int:
        if self._data_type_hash is None:
            self._data_type_hash = self.__class__._calculate_data_type_hash(
                self._type_config
            )
        return self._data_type_hash

    @property
    def characteristics(self) -> DataTypeCharacteristics:
        if self._characteristics is not None:
            return self._characteristics

        self._characteristics = self._retrieve_characteristics()
        return self._characteristics

    def _retrieve_characteristics(self) -> DataTypeCharacteristics:
        return DataTypeCharacteristics()

    # @abc.abstractmethod
    # def is_immutable(self) -> bool:
    #     pass

    def calculate_hash(self, data: "SerializedData") -> str:
        """Calculate the hash of the value."""

        return data.instance_id

    def calculate_size(self, data: "SerializedData") -> int:
        """Calculate the size of the value."""

        return data.data_size

    def serialize_as_json(self, data: Any) -> "SerializedData":

        _data = {"data": {"type": "inline-json", "inline_data": data, "codec": "json"}}

        serialized_data = {
            "data_type": self.data_type_name,
            "data_type_config": self.type_config.dict(),
            "data": _data,
            "serialization_profile": "json",
            "metadata": {
                "environment": {},
                "deserialize": {
                    "python_object": {
                        "module_type": "deserialize.from_json",
                        "module_config": {"result_path": "data"},
                    }
                },
            },
        }
        from kiara.models.values.value import SerializationResult

        serialized = SerializationResult(**serialized_data)
        return serialized

    def serialize(self, data: TYPE_PYTHON_CLS) -> Union[None, str, "SerializedData"]:

        logger.debug(
            "ignore.serialize_request",
            data_type=self.data_type_name,
            reason="no 'serialize' method imnplemented",
        )
        return NO_SERIALIZATION_MARKER
        # raise NotImplementedError(f"Data type '{self.data_type_name}' does not support serialization.")
        #
        # try:
        #     import pickle5 as pickle
        # except Exception:
        #     import pickle  # type: ignore
        #
        # pickled = pickle.dumps(data, protocol=5)
        # _data = {"python_object": {"type": "chunk", "chunk": pickled, "codec": "raw"}}
        #
        # serialized_data = {
        #     "data_type": self.data_type_name,
        #     "data_type_config": self.type_config.dict(),
        #     "data": _data,
        #     "serialization_profile": "pickle",
        #     "serialization_metadata": {
        #         "profile": "pickle",
        #         "environment": {},
        #         "deserialize": {
        #             "object": {
        #                 "module_name": "value.unpickle",
        #                 "module_config": {
        #                     "value_type": "any",
        #                     "target_profile": "object",
        #                     "serialization_profile": "pickle",
        #                 },
        #             }
        #         },
        #     },
        # }
        # from kiara.models.values.value import SerializationResult
        #
        # serialized = SerializationResult(**serialized_data)
        # return serialized

    @property
    def type_config(self) -> TYPE_CONFIG_CLS:
        return self._type_config

    def _pre_examine_data(
        self, data: Any, schema: ValueSchema
    ) -> Tuple[Any, Union[str, "SerializedData"], ValueStatus, str, int]:

        if data == SpecialValue.NOT_SET:
            status = ValueStatus.NOT_SET
            data = None
        else:
            status = ValueStatus.SET

        if data is None and schema.default not in [
            None,
            SpecialValue.NO_VALUE,
            SpecialValue.NOT_SET,
        ]:

            status = ValueStatus.DEFAULT
            if callable(schema.default):
                data = schema.default()
            else:
                data = copy.deepcopy(schema.default)

        if data in [None, SpecialValue.NO_VALUE, SpecialValue.NOT_SET]:
            if schema.default in [None, SpecialValue.NO_VALUE]:
                data = SpecialValue.NO_VALUE
                status = ValueStatus.NONE
            elif schema.default == SpecialValue.NOT_SET:
                data = SpecialValue.NOT_SET
                status = ValueStatus.NOT_SET

            size = 0
            value_hash = INVALID_HASH_MARKER
            serialized: Union[None, str, "SerializedData"] = NO_SERIALIZATION_MARKER
        else:

            from kiara.models.values.value import SerializedData

            if isinstance(data, SerializedData):
                # TODO: assert value is in schema lineage
                # assert data.data_type == schema.type
                # assert data.data_type_config == schema.type_config
                serialized = data
                not_serialized: bool = False
            else:
                data = self.parse_python_obj(data)
                if data is None:
                    raise Exception(
                        f"Invalid data, can't parse into a value of type '{schema.type}'."
                    )
                self._validate(data)

                serialized = self.serialize(data)
                if serialized is None:
                    serialized = NO_SERIALIZATION_MARKER

                if isinstance(serialized, str):
                    not_serialized = True
                else:
                    not_serialized = False

            if not_serialized:
                size = INVALID_SIZE_MARKER
                value_hash = INVALID_HASH_MARKER
            else:
                size = serialized.data_size  # type: ignore
                value_hash = serialized.instance_id  # type: ignore

        assert serialized is not None
        result = (data, serialized, status, value_hash, size)
        return result

    def assemble_value(
        self,
        value_id: uuid.UUID,
        data: Any,
        schema: ValueSchema,
        serialized: Union[str, "SerializedData"],
        status: Union[ValueStatus, str],
        value_hash: str,
        value_size: int,
        pedigree: "ValuePedigree",
        kiara_id: uuid.UUID,
        pedigree_output_name: str,
    ) -> Tuple["Value", Any]:

        from kiara.models.values.value import Value

        if isinstance(status, str):
            status = ValueStatus(status).name

        this_cls = PythonClass.from_class(self.__class__)
        if status in [ValueStatus.SET, ValueStatus.DEFAULT]:
            try:

                value = Value(
                    value_id=value_id,
                    kiara_id=kiara_id,
                    value_status=status,
                    value_size=value_size,
                    value_hash=value_hash,
                    value_schema=schema,
                    pedigree=pedigree,
                    pedigree_output_name=pedigree_output_name,
                    data_type_class=this_cls,
                )

            except Exception as e:
                raise KiaraValueException(
                    data_type=self.__class__, value_data=data, exception=e
                )
        else:
            value = Value(
                value_id=value_id,
                kiara_id=kiara_id,
                value_status=status,
                value_size=value_size,
                value_hash=value_hash,
                value_schema=schema,
                pedigree=pedigree,
                pedigree_output_name=pedigree_output_name,
                data_type_class=this_cls,
            )

        value._value_data = data
        value._data_type = self
        value._serialized_data = serialized
        return value, data

    def parse_python_obj(self, data: Any) -> TYPE_PYTHON_CLS:
        """Parse a value into a supported python type.

        This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object).
        If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to
        avoid adding or removing information from the data (e.g. by changing the resolution of a date).

        Arguments:
            v: the value

        Returns:
            'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object
        """

        return data

    def _validate(self, value: TYPE_PYTHON_CLS) -> None:
        """Validate the value. This expects an instance of the defined Python class (from 'backing_python_type)."""

        if not isinstance(value, self.__class__.python_class()):
            raise ValueError(
                f"Invalid python type '{type(value)}', must be: {self.__class__.python_class()}"
            )

    def create_renderable(self, **config):

        show_type_info = config.get("show_type_info", False)

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("key")
        table.add_column("value", style="i")
        table.add_row("type_name", self.data_type_name)
        config_json = self.type_config.json(
            exclude_unset=True, option=orjson.OPT_INDENT_2
        )
        config = Syntax(config_json, "json", background_color="default")
        table.add_row("type_config", config)

        if show_type_info:
            from kiara.models.values.data_type import DataTypeClassInfo

            info = DataTypeClassInfo.create_from_type_class(self.__class__)
            table.add_row("", "")
            table.add_row("", Rule())
            table.add_row("type_info", info)

        return table

characteristics: DataTypeCharacteristics property readonly

data_type_hash: int property readonly

data_type_name: str property readonly

type_config: ~TYPE_CONFIG_CLS property readonly

Methods

assemble_value(self, value_id, data, schema, serialized, status, value_hash, value_size, pedigree, kiara_id, pedigree_output_name)

Source code in kiara/data_types/__init__.py

def assemble_value(
    self,
    value_id: uuid.UUID,
    data: Any,
    schema: ValueSchema,
    serialized: Union[str, "SerializedData"],
    status: Union[ValueStatus, str],
    value_hash: str,
    value_size: int,
    pedigree: "ValuePedigree",
    kiara_id: uuid.UUID,
    pedigree_output_name: str,
) -> Tuple["Value", Any]:

    from kiara.models.values.value import Value

    if isinstance(status, str):
        status = ValueStatus(status).name

    this_cls = PythonClass.from_class(self.__class__)
    if status in [ValueStatus.SET, ValueStatus.DEFAULT]:
        try:

            value = Value(
                value_id=value_id,
                kiara_id=kiara_id,
                value_status=status,
                value_size=value_size,
                value_hash=value_hash,
                value_schema=schema,
                pedigree=pedigree,
                pedigree_output_name=pedigree_output_name,
                data_type_class=this_cls,
            )

        except Exception as e:
            raise KiaraValueException(
                data_type=self.__class__, value_data=data, exception=e
            )
    else:
        value = Value(
            value_id=value_id,
            kiara_id=kiara_id,
            value_status=status,
            value_size=value_size,
            value_hash=value_hash,
            value_schema=schema,
            pedigree=pedigree,
            pedigree_output_name=pedigree_output_name,
            data_type_class=this_cls,
        )

    value._value_data = data
    value._data_type = self
    value._serialized_data = serialized
    return value, data

calculate_hash(self, data)

Calculate the hash of the value.

Source code in kiara/data_types/__init__.py

def calculate_hash(self, data: "SerializedData") -> str:
    """Calculate the hash of the value."""

    return data.instance_id

calculate_size(self, data)

Calculate the size of the value.

Source code in kiara/data_types/__init__.py

def calculate_size(self, data: "SerializedData") -> int:
    """Calculate the size of the value."""

    return data.data_size

create_renderable(self, **config)

Source code in kiara/data_types/__init__.py

def create_renderable(self, **config):

    show_type_info = config.get("show_type_info", False)

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("key")
    table.add_column("value", style="i")
    table.add_row("type_name", self.data_type_name)
    config_json = self.type_config.json(
        exclude_unset=True, option=orjson.OPT_INDENT_2
    )
    config = Syntax(config_json, "json", background_color="default")
    table.add_row("type_config", config)

    if show_type_info:
        from kiara.models.values.data_type import DataTypeClassInfo

        info = DataTypeClassInfo.create_from_type_class(self.__class__)
        table.add_row("", "")
        table.add_row("", Rule())
        table.add_row("type_info", info)

    return table

data_type_config_class() classmethod

Source code in kiara/data_types/__init__.py

@classmethod
def data_type_config_class(cls) -> Type[TYPE_CONFIG_CLS]:
    return DataTypeConfig  # type: ignore

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
~TYPE_PYTHON_CLS	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/__init__.py

def parse_python_obj(self, data: Any) -> TYPE_PYTHON_CLS:
    """Parse a value into a supported python type.

    This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object).
    If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to
    avoid adding or removing information from the data (e.g. by changing the resolution of a date).

    Arguments:
        v: the value

    Returns:
        'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object
    """

    return data

python_class() classmethod

Source code in kiara/data_types/__init__.py

@classmethod
@abc.abstractmethod
def python_class(cls) -> Type[TYPE_PYTHON_CLS]:
    pass

retrieve_available_type_profiles() classmethod

Source code in kiara/data_types/__init__.py

@classmethod
def retrieve_available_type_profiles(cls) -> Mapping[str, Mapping[str, Any]]:
    return {}

serialize(self, data)

Source code in kiara/data_types/__init__.py

def serialize(self, data: TYPE_PYTHON_CLS) -> Union[None, str, "SerializedData"]:

    logger.debug(
        "ignore.serialize_request",
        data_type=self.data_type_name,
        reason="no 'serialize' method imnplemented",
    )
    return NO_SERIALIZATION_MARKER
    # raise NotImplementedError(f"Data type '{self.data_type_name}' does not support serialization.")
    #
    # try:
    #     import pickle5 as pickle
    # except Exception:
    #     import pickle  # type: ignore
    #
    # pickled = pickle.dumps(data, protocol=5)
    # _data = {"python_object": {"type": "chunk", "chunk": pickled, "codec": "raw"}}
    #
    # serialized_data = {
    #     "data_type": self.data_type_name,
    #     "data_type_config": self.type_config.dict(),
    #     "data": _data,
    #     "serialization_profile": "pickle",
    #     "serialization_metadata": {
    #         "profile": "pickle",
    #         "environment": {},
    #         "deserialize": {
    #             "object": {
    #                 "module_name": "value.unpickle",
    #                 "module_config": {
    #                     "value_type": "any",
    #                     "target_profile": "object",
    #                     "serialization_profile": "pickle",
    #                 },
    #             }
    #         },
    #     },
    # }
    # from kiara.models.values.value import SerializationResult
    #
    # serialized = SerializationResult(**serialized_data)
    # return serialized

serialize_as_json(self, data)

Source code in kiara/data_types/__init__.py

def serialize_as_json(self, data: Any) -> "SerializedData":

    _data = {"data": {"type": "inline-json", "inline_data": data, "codec": "json"}}

    serialized_data = {
        "data_type": self.data_type_name,
        "data_type_config": self.type_config.dict(),
        "data": _data,
        "serialization_profile": "json",
        "metadata": {
            "environment": {},
            "deserialize": {
                "python_object": {
                    "module_type": "deserialize.from_json",
                    "module_config": {"result_path": "data"},
                }
            },
        },
    }
    from kiara.models.values.value import SerializationResult

    serialized = SerializationResult(**serialized_data)
    return serialized

DataTypeCharacteristics (BaseModel) pydantic-model

Source code in kiara/data_types/__init__.py

class DataTypeCharacteristics(BaseModel):

    is_scalar: bool = Field(
        description="Whether the data desribed by this data type behaves like a skalar.",
        default=False,
    )
    is_json_serializable: bool = Field(
        description="Whether the data can be serialized to json without information loss.",
        default=False,
    )

Attributes

is_json_serializable: bool pydantic-field

Whether the data can be serialized to json without information loss.

is_scalar: bool pydantic-field

Whether the data desribed by this data type behaves like a skalar.

DataTypeConfig (BaseModel) pydantic-model

Base class that describes the configuration a [DataType][kiara.data.data_types.DataType] class accepts.

This is stored in the _config_cls class attribute in each DataType class. By default, a DataType is not configurable, unless the _config_cls class attribute points to a sub-class of this class.

Source code in kiara/data_types/__init__.py

class DataTypeConfig(BaseModel):
    """Base class that describes the configuration a [``DataType``][kiara.data.data_types.DataType] class accepts.

    This is stored in the ``_config_cls`` class attribute in each ``DataType`` class. By default,
    a ``DataType`` is not configurable, unless the ``_config_cls`` class attribute points to a sub-class of this class.
    """

    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps
        extra = Extra.forbid

    @classmethod
    def requires_config(cls) -> bool:
        """Return whether this class can be used as-is, or requires configuration before an instance can be created."""

        for field_name, field in cls.__fields__.items():
            if field.required and field.default is None:
                return True
        return False

    _config_hash: Optional[int] = PrivateAttr(default=None)

    def get(self, key: str) -> Any:
        """Get the value for the specified configuation key."""

        if key not in self.__fields__:
            raise Exception(
                f"No config value '{key}' in module config class '{self.__class__.__name__}'."
            )

        return getattr(self, key)

    @property
    def config_hash(self) -> int:

        if self._config_hash is None:
            _d = self.dict()
            hashes = DeepHash(_d)
            self._config_hash = hashes[_d]
        return self._config_hash

    def __eq__(self, other):

        if self.__class__ != other.__class__:
            return False

        return self.dict() == other.dict()

    def __hash__(self):

        return self.config_hash

    def __rich_console__(
        self, console: Console, options: ConsoleOptions
    ) -> RenderResult:

        my_table = Table(box=box.MINIMAL, show_header=False)
        my_table.add_column("Field name", style="i")
        my_table.add_column("Value")
        for field in self.__fields__:
            my_table.add_row(field, getattr(self, field))

        yield my_table

config_hash: int property readonly

Config

Source code in kiara/data_types/__init__.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps
    extra = Extra.forbid

extra

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/data_types/__init__.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

Methods

get(self, key)

Get the value for the specified configuation key.

Source code in kiara/data_types/__init__.py

def get(self, key: str) -> Any:
    """Get the value for the specified configuation key."""

    if key not in self.__fields__:
        raise Exception(
            f"No config value '{key}' in module config class '{self.__class__.__name__}'."
        )

    return getattr(self, key)

requires_config() classmethod

Return whether this class can be used as-is, or requires configuration before an instance can be created.

Source code in kiara/data_types/__init__.py

@classmethod
def requires_config(cls) -> bool:
    """Return whether this class can be used as-is, or requires configuration before an instance can be created."""

    for field_name, field in cls.__fields__.items():
        if field.required and field.default is None:
            return True
    return False

Modules

included_core_types special

KIARA_MODEL_CLS

SCALAR_CHARACTERISTICS

Classes

AnyType (DataType, Generic)

'Any' type, the parent type for most other types.

This type acts as the parents for all (or at least most) non-internal value types. There are some generic operations (like 'persist_value', or 'pretty_print') which are implemented for this type, so it's descendents have a fallback option in case no subtype-specific operations are implemented for it. In general, it is not recommended to use the 'any' type as module input or output, but it is possible. Values of type 'any' are not allowed to be persisted (at the moment, this might or might not change).

Source code in kiara/data_types/included_core_types/__init__.py

class AnyType(
    DataType[TYPE_PYTHON_CLS, DataTypeConfig], Generic[TYPE_PYTHON_CLS, TYPE_CONFIG_CLS]
):
    """'Any' type, the parent type for most other types.

    This type acts as the parents for all (or at least most) non-internal value types. There are some generic operations
    (like 'persist_value', or 'pretty_print') which are implemented for this type, so it's descendents have a fallback
    option in case no subtype-specific operations are implemented for it. In general, it is not recommended to use the 'any'
    type as module input or output, but it is possible. Values of type 'any' are not allowed to be persisted (at the moment,
    this might or might not change).
    """

    _data_type_name = "any"

    @classmethod
    def python_class(cls) -> Type:
        return object

    def pretty_print_as__string(
        self, value: "Value", render_config: Mapping[str, Any]
    ) -> Any:

        data = value.data
        return str(data)

pretty_print_as__string(self, value, render_config)

Source code in kiara/data_types/included_core_types/__init__.py

def pretty_print_as__string(
    self, value: "Value", render_config: Mapping[str, Any]
) -> Any:

    data = value.data
    return str(data)

python_class() classmethod

Source code in kiara/data_types/included_core_types/__init__.py

@classmethod
def python_class(cls) -> Type:
    return object

BytesType (AnyType)

An array of bytes.

Source code in kiara/data_types/included_core_types/__init__.py

class BytesType(AnyType[bytes, DataTypeConfig]):
    """An array of bytes."""

    _data_type_name = "bytes"

    @classmethod
    def python_class(cls) -> Type:
        return bytes

    def serialize(self, data: bytes) -> "SerializedData":

        _data = {"bytes": {"type": "chunk", "chunk": data, "codec": "raw"}}

        serialized_data = {
            "data_type": self.data_type_name,
            "data_type_config": self.type_config.dict(),
            "data": _data,
            "serialization_profile": "raw",
            "metadata": {
                "environment": {},
                "deserialize": {
                    "python_object": {
                        "module_name": "load.bytes",
                        "module_config": {
                            "value_type": "bytes",
                            "target_profile": "python_object",
                            "serialization_profile": "raw",
                        },
                    }
                },
            },
        }
        from kiara.models.values.value import SerializationResult

        serialized = SerializationResult(**serialized_data)
        return serialized

    def pretty_print_as__string(
        self, value: "Value", render_config: Mapping[str, Any]
    ) -> Any:

        data: bytes = value.data
        return data.decode()

pretty_print_as__string(self, value, render_config)

Source code in kiara/data_types/included_core_types/__init__.py

def pretty_print_as__string(
    self, value: "Value", render_config: Mapping[str, Any]
) -> Any:

    data: bytes = value.data
    return data.decode()

python_class() classmethod

Source code in kiara/data_types/included_core_types/__init__.py

@classmethod
def python_class(cls) -> Type:
    return bytes

serialize(self, data)

Source code in kiara/data_types/included_core_types/__init__.py

def serialize(self, data: bytes) -> "SerializedData":

    _data = {"bytes": {"type": "chunk", "chunk": data, "codec": "raw"}}

    serialized_data = {
        "data_type": self.data_type_name,
        "data_type_config": self.type_config.dict(),
        "data": _data,
        "serialization_profile": "raw",
        "metadata": {
            "environment": {},
            "deserialize": {
                "python_object": {
                    "module_name": "load.bytes",
                    "module_config": {
                        "value_type": "bytes",
                        "target_profile": "python_object",
                        "serialization_profile": "raw",
                    },
                }
            },
        },
    }
    from kiara.models.values.value import SerializationResult

    serialized = SerializationResult(**serialized_data)
    return serialized

KiaraModelValueType (AnyType, Generic)

A value type that is used internally.

This type should not be used by user-facing modules and/or operations.

Source code in kiara/data_types/included_core_types/__init__.py

class KiaraModelValueType(
    AnyType[KIARA_MODEL_CLS, TYPE_CONFIG_CLS], Generic[KIARA_MODEL_CLS, TYPE_CONFIG_CLS]
):
    """A value type that is used internally.

    This type should not be used by user-facing modules and/or operations.
    """

    _data_type_name = None  # type: ignore

    @classmethod
    def data_type_config_class(cls) -> Type[DataTypeConfig]:
        return DataTypeConfig

    @abc.abstractmethod
    def create_model_from_python_obj(self, data: Any) -> KIARA_MODEL_CLS:
        pass

    def parse_python_obj(self, data: Any) -> KIARA_MODEL_CLS:

        if isinstance(data, self.__class__.python_class()):
            return data  # type: ignore

        data = self.create_model_from_python_obj(data)
        return data

    def _validate(self, data: KiaraModel) -> None:

        if not isinstance(data, self.__class__.python_class()):
            raise Exception(
                f"Invalid type '{type(data)}', must be: {self.__class__.python_class().__name__}, or subclass."
            )

Methods

create_model_from_python_obj(self, data)

Source code in kiara/data_types/included_core_types/__init__.py

@abc.abstractmethod
def create_model_from_python_obj(self, data: Any) -> KIARA_MODEL_CLS:
    pass

data_type_config_class() classmethod

Source code in kiara/data_types/included_core_types/__init__.py

@classmethod
def data_type_config_class(cls) -> Type[DataTypeConfig]:
    return DataTypeConfig

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
~KIARA_MODEL_CLS	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/included_core_types/__init__.py

def parse_python_obj(self, data: Any) -> KIARA_MODEL_CLS:

    if isinstance(data, self.__class__.python_class()):
        return data  # type: ignore

    data = self.create_model_from_python_obj(data)
    return data

NoneType (DataType)

Type indicating a 'None' value

Source code in kiara/data_types/included_core_types/__init__.py

class NoneType(DataType[SpecialValue, DataTypeConfig]):
    """Type indicating a 'None' value"""

    _data_type_name = "none"

    @classmethod
    def python_class(cls) -> Type:
        return SpecialValue

    # def is_immutable(self) -> bool:
    #     return False

    def calculate_hash(self, data: Any) -> str:
        return INVALID_HASH_MARKER

    def calculate_size(self, data: Any) -> int:
        return 0

    def parse_python_obj(self, data: Any) -> SpecialValue:
        return SpecialValue.NO_VALUE

    def pretty_print_as__string(
        self, value: "Value", render_config: Mapping[str, Any]
    ) -> Any:

        data = value.data
        return str(data.value)

Methods

calculate_hash(self, data)

Calculate the hash of the value.

Source code in kiara/data_types/included_core_types/__init__.py

def calculate_hash(self, data: Any) -> str:
    return INVALID_HASH_MARKER

calculate_size(self, data)

Calculate the size of the value.

Source code in kiara/data_types/included_core_types/__init__.py

def calculate_size(self, data: Any) -> int:
    return 0

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
SpecialValue	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/included_core_types/__init__.py

def parse_python_obj(self, data: Any) -> SpecialValue:
    return SpecialValue.NO_VALUE

pretty_print_as__string(self, value, render_config)

Source code in kiara/data_types/included_core_types/__init__.py

def pretty_print_as__string(
    self, value: "Value", render_config: Mapping[str, Any]
) -> Any:

    data = value.data
    return str(data.value)

python_class() classmethod

Source code in kiara/data_types/included_core_types/__init__.py

@classmethod
def python_class(cls) -> Type:
    return SpecialValue

StringType (AnyType)

A string.

Source code in kiara/data_types/included_core_types/__init__.py

class StringType(AnyType[str, DataTypeConfig]):
    """A string."""

    _data_type_name = "string"

    @classmethod
    def python_class(cls) -> Type:
        return str

    def serialize(self, data: str) -> "SerializedData":

        _data = {
            "string": {"type": "chunk", "chunk": data.encode("utf-8"), "codec": "raw"}
        }

        serialized_data = {
            "data_type": self.data_type_name,
            "data_type_config": self.type_config.dict(),
            "data": _data,
            "serialization_profile": "raw",
            "metadata": {
                "environment": {},
                "deserialize": {
                    "python_object": {
                        "module_type": "load.string",
                        "module_config": {
                            "value_type": "string",
                            "target_profile": "python_object",
                            "serialization_profile": "raw",
                        },
                    }
                },
            },
        }
        from kiara.models.values.value import SerializationResult

        serialized = SerializationResult(**serialized_data)
        return serialized

    def _retrieve_characteristics(self) -> DataTypeCharacteristics:
        return SCALAR_CHARACTERISTICS

    def _validate(cls, value: Any) -> None:

        if not isinstance(value, str):
            raise ValueError(f"Invalid type '{type(value)}': string required")

    def pretty_print_as__bytes(self, value: "Value", render_config: Mapping[str, Any]):
        value_str: str = value.data
        return value_str.encode()

pretty_print_as__bytes(self, value, render_config)

Source code in kiara/data_types/included_core_types/__init__.py

def pretty_print_as__bytes(self, value: "Value", render_config: Mapping[str, Any]):
    value_str: str = value.data
    return value_str.encode()

python_class() classmethod

Source code in kiara/data_types/included_core_types/__init__.py

@classmethod
def python_class(cls) -> Type:
    return str

serialize(self, data)

Source code in kiara/data_types/included_core_types/__init__.py

def serialize(self, data: str) -> "SerializedData":

    _data = {
        "string": {"type": "chunk", "chunk": data.encode("utf-8"), "codec": "raw"}
    }

    serialized_data = {
        "data_type": self.data_type_name,
        "data_type_config": self.type_config.dict(),
        "data": _data,
        "serialization_profile": "raw",
        "metadata": {
            "environment": {},
            "deserialize": {
                "python_object": {
                    "module_type": "load.string",
                    "module_config": {
                        "value_type": "string",
                        "target_profile": "python_object",
                        "serialization_profile": "raw",
                    },
                }
            },
        },
    }
    from kiara.models.values.value import SerializationResult

    serialized = SerializationResult(**serialized_data)
    return serialized

Modules

filesystem

SUPPORTED_FILE_TYPES

logger

Classes

FileBundleValueType (AnyType)

A bundle of files (like a folder, zip archive, etc.).

Source code in kiara/data_types/included_core_types/filesystem.py

class FileBundleValueType(AnyType[FileBundle, FileTypeConfig]):
    """A bundle of files (like a folder, zip archive, etc.)."""

    _data_type_name = "file_bundle"

    @classmethod
    def retrieve_available_type_profiles(cls) -> Mapping[str, Mapping[str, Any]]:
        result = {}
        for ft in SUPPORTED_FILE_TYPES:
            result[f"{ft}_file_bundle"] = {"content_type": ft}
        return result

    @classmethod
    def python_class(cls) -> Type:
        return FileBundle

    @classmethod
    def data_type_config_class(cls) -> Type[FileTypeConfig]:
        return FileTypeConfig

    def serialize(self, data: FileBundle) -> "SerializedData":

        file_data: Dict[str, Any] = {}
        file_metadata = {}
        for rel_path, file in data.included_files.items():
            file_data[rel_path] = {"type": "file", "codec": "raw", "file": file.path}
            file_metadata[rel_path] = {
                "file_name": file.file_name,
                "import_time": file.import_time,
            }

        metadata: Dict[str, Any] = {
            "included_files": file_metadata,
            "bundle_name": data.bundle_name,
            "import_time": data.import_time,
            "size": data.size,
            "number_of_files": data.number_of_files,
        }

        assert "__file_metadata__" not in file_data
        file_data["__file_metadata__"] = {
            "type": "inline-json",
            "codec": "json",
            "inline_data": metadata,
        }

        serialized_data = {
            "data_type": self.data_type_name,
            "data_type_config": self.type_config.dict(),
            "data": file_data,
            "serialization_profile": "copy",
            "metadata": {
                "environment": {},
                "deserialize": {
                    "python_object": {
                        "module_type": "deserialize.file_bundle",
                        "module_config": {
                            "value_type": "file_bundle",
                            "target_profile": "python_object",
                            "serialization_profile": "copy",
                        },
                    }
                },
            },
        }
        from kiara.models.values.value import SerializationResult

        serialized = SerializationResult(**serialized_data)
        return serialized

    def parse_python_obj(self, data: Any) -> FileBundle:

        if isinstance(data, FileBundle):
            return data
        elif isinstance(data, str):
            return FileBundle.import_folder(source=data)
        else:
            raise Exception(
                f"Can't create FileBundle from data of type '{type(data)}'."
            )

    def pretty_print_as__terminal_renderable(
        self, value: "Value", render_config: Mapping[str, Any]
    ) -> Any:

        bundle: FileBundle = value.data
        renderable = bundle.create_renderable(**render_config)
        return renderable

Methods

data_type_config_class() classmethod

Source code in kiara/data_types/included_core_types/filesystem.py

@classmethod
def data_type_config_class(cls) -> Type[FileTypeConfig]:
    return FileTypeConfig

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
FileBundle	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/included_core_types/filesystem.py

def parse_python_obj(self, data: Any) -> FileBundle:

    if isinstance(data, FileBundle):
        return data
    elif isinstance(data, str):
        return FileBundle.import_folder(source=data)
    else:
        raise Exception(
            f"Can't create FileBundle from data of type '{type(data)}'."
        )

pretty_print_as__terminal_renderable(self, value, render_config)

Source code in kiara/data_types/included_core_types/filesystem.py

def pretty_print_as__terminal_renderable(
    self, value: "Value", render_config: Mapping[str, Any]
) -> Any:

    bundle: FileBundle = value.data
    renderable = bundle.create_renderable(**render_config)
    return renderable

python_class() classmethod

Source code in kiara/data_types/included_core_types/filesystem.py

@classmethod
def python_class(cls) -> Type:
    return FileBundle

retrieve_available_type_profiles() classmethod

Source code in kiara/data_types/included_core_types/filesystem.py

@classmethod
def retrieve_available_type_profiles(cls) -> Mapping[str, Mapping[str, Any]]:
    result = {}
    for ft in SUPPORTED_FILE_TYPES:
        result[f"{ft}_file_bundle"] = {"content_type": ft}
    return result

serialize(self, data)

Source code in kiara/data_types/included_core_types/filesystem.py

def serialize(self, data: FileBundle) -> "SerializedData":

    file_data: Dict[str, Any] = {}
    file_metadata = {}
    for rel_path, file in data.included_files.items():
        file_data[rel_path] = {"type": "file", "codec": "raw", "file": file.path}
        file_metadata[rel_path] = {
            "file_name": file.file_name,
            "import_time": file.import_time,
        }

    metadata: Dict[str, Any] = {
        "included_files": file_metadata,
        "bundle_name": data.bundle_name,
        "import_time": data.import_time,
        "size": data.size,
        "number_of_files": data.number_of_files,
    }

    assert "__file_metadata__" not in file_data
    file_data["__file_metadata__"] = {
        "type": "inline-json",
        "codec": "json",
        "inline_data": metadata,
    }

    serialized_data = {
        "data_type": self.data_type_name,
        "data_type_config": self.type_config.dict(),
        "data": file_data,
        "serialization_profile": "copy",
        "metadata": {
            "environment": {},
            "deserialize": {
                "python_object": {
                    "module_type": "deserialize.file_bundle",
                    "module_config": {
                        "value_type": "file_bundle",
                        "target_profile": "python_object",
                        "serialization_profile": "copy",
                    },
                }
            },
        },
    }
    from kiara.models.values.value import SerializationResult

    serialized = SerializationResult(**serialized_data)
    return serialized

FileTypeConfig (DataTypeConfig) pydantic-model

Source code in kiara/data_types/included_core_types/filesystem.py

class FileTypeConfig(DataTypeConfig):

    content_type: Optional[str] = Field(
        description="The content type of this file.", default=None
    )

Attributes

content_type: str pydantic-field

The content type of this file.

FileValueType (KiaraModelValueType)

A file.

Source code in kiara/data_types/included_core_types/filesystem.py

class FileValueType(KiaraModelValueType[FileModel, FileTypeConfig]):
    """A file."""

    _data_type_name = "file"

    @classmethod
    def retrieve_available_type_profiles(cls) -> Mapping[str, Mapping[str, Any]]:
        result = {}
        for ft in SUPPORTED_FILE_TYPES:
            result[f"{ft}_file"] = {"content_type": ft}
        return result

    @classmethod
    def python_class(cls) -> Type:
        return FileModel

    @classmethod
    def data_type_config_class(cls) -> Type[FileTypeConfig]:
        return FileTypeConfig

    def serialize(self, data: FileModel) -> "SerializedData":

        _data = {
            data.file_name: {
                "type": "file",
                "codec": "raw",
                "file": data.path,
            },
            "__file_metadata__": {
                "type": "inline-json",
                "codec": "json",
                "inline_data": {
                    "file_name": data.file_name,
                    "import_time": data.import_time,
                },
            },
        }

        serialized_data = {
            "data_type": self.data_type_name,
            "data_type_config": self.type_config.dict(),
            "data": _data,
            "serialization_profile": "copy",
            "metadata": {
                # "profile": "",
                "environment": {},
                "deserialize": {
                    "python_object": {
                        "module_type": "deserialize.file",
                        "module_config": {
                            "value_type": "file",
                            "target_profile": "python_object",
                            "serialization_profile": "copy",
                        },
                    }
                },
            },
        }
        from kiara.models.values.value import SerializationResult

        serialized = SerializationResult(**serialized_data)
        return serialized

    def create_model_from_python_obj(self, data: Any) -> FileModel:

        if isinstance(data, Mapping):
            return FileModel(**data)
        if isinstance(data, str):
            return FileModel.load_file(source=data)
        else:
            raise Exception(f"Can't create FileModel from data of type '{type(data)}'.")

    def pretty_print_as__string(
        self, value: "Value", render_config: Mapping[str, Any]
    ) -> Any:

        data: Any = value.data
        max_lines = render_config.get("max_lines", 34)
        try:
            lines = []
            with open(data.path, "r", encoding="utf-8") as f:
                for idx, l in enumerate(f):
                    if idx > max_lines:
                        lines.append("...\n")
                        lines.append("...")
                        break
                    lines.append(l)

            # TODO: syntax highlighting
            return "\n".join(lines)
        except UnicodeDecodeError:
            # found non-text data
            lines = [
                "Binary file or non-utf8 enconding, not printing content...",
                "",
                "[b]File metadata:[/b]",
                "",
                data.json(option=orjson.OPT_INDENT_2),
            ]
            return "\n".join("lines")

    def pretty_print_as__terminal_renderable(
        self, value: "Value", render_config: Mapping[str, Any]
    ) -> Any:

        data: Any = value.data
        max_lines = render_config.get("max_lines", 34)
        try:
            lines = []
            with open(data.path, "r", encoding="utf-8") as f:
                for idx, l in enumerate(f):
                    if idx > max_lines:
                        lines.append("...\n")
                        lines.append("...")
                        break
                    lines.append(l.rstrip())

            return Group(*lines)
        except UnicodeDecodeError:
            # found non-text data
            lines = [
                "Binary file or non-utf8 enconding, not printing content...",
                "",
                "[b]File metadata:[/b]",
                "",
                data.json(option=orjson.OPT_INDENT_2),
            ]
            return Group(*lines)

create_model_from_python_obj(self, data)

Source code in kiara/data_types/included_core_types/filesystem.py

def create_model_from_python_obj(self, data: Any) -> FileModel:

    if isinstance(data, Mapping):
        return FileModel(**data)
    if isinstance(data, str):
        return FileModel.load_file(source=data)
    else:
        raise Exception(f"Can't create FileModel from data of type '{type(data)}'.")

data_type_config_class() classmethod

Source code in kiara/data_types/included_core_types/filesystem.py

@classmethod
def data_type_config_class(cls) -> Type[FileTypeConfig]:
    return FileTypeConfig

pretty_print_as__string(self, value, render_config)

Source code in kiara/data_types/included_core_types/filesystem.py

def pretty_print_as__string(
    self, value: "Value", render_config: Mapping[str, Any]
) -> Any:

    data: Any = value.data
    max_lines = render_config.get("max_lines", 34)
    try:
        lines = []
        with open(data.path, "r", encoding="utf-8") as f:
            for idx, l in enumerate(f):
                if idx > max_lines:
                    lines.append("...\n")
                    lines.append("...")
                    break
                lines.append(l)

        # TODO: syntax highlighting
        return "\n".join(lines)
    except UnicodeDecodeError:
        # found non-text data
        lines = [
            "Binary file or non-utf8 enconding, not printing content...",
            "",
            "[b]File metadata:[/b]",
            "",
            data.json(option=orjson.OPT_INDENT_2),
        ]
        return "\n".join("lines")

pretty_print_as__terminal_renderable(self, value, render_config)

Source code in kiara/data_types/included_core_types/filesystem.py

def pretty_print_as__terminal_renderable(
    self, value: "Value", render_config: Mapping[str, Any]
) -> Any:

    data: Any = value.data
    max_lines = render_config.get("max_lines", 34)
    try:
        lines = []
        with open(data.path, "r", encoding="utf-8") as f:
            for idx, l in enumerate(f):
                if idx > max_lines:
                    lines.append("...\n")
                    lines.append("...")
                    break
                lines.append(l.rstrip())

        return Group(*lines)
    except UnicodeDecodeError:
        # found non-text data
        lines = [
            "Binary file or non-utf8 enconding, not printing content...",
            "",
            "[b]File metadata:[/b]",
            "",
            data.json(option=orjson.OPT_INDENT_2),
        ]
        return Group(*lines)

python_class() classmethod

Source code in kiara/data_types/included_core_types/filesystem.py

@classmethod
def python_class(cls) -> Type:
    return FileModel

retrieve_available_type_profiles() classmethod

Source code in kiara/data_types/included_core_types/filesystem.py

@classmethod
def retrieve_available_type_profiles(cls) -> Mapping[str, Mapping[str, Any]]:
    result = {}
    for ft in SUPPORTED_FILE_TYPES:
        result[f"{ft}_file"] = {"content_type": ft}
    return result

serialize(self, data)

Source code in kiara/data_types/included_core_types/filesystem.py

def serialize(self, data: FileModel) -> "SerializedData":

    _data = {
        data.file_name: {
            "type": "file",
            "codec": "raw",
            "file": data.path,
        },
        "__file_metadata__": {
            "type": "inline-json",
            "codec": "json",
            "inline_data": {
                "file_name": data.file_name,
                "import_time": data.import_time,
            },
        },
    }

    serialized_data = {
        "data_type": self.data_type_name,
        "data_type_config": self.type_config.dict(),
        "data": _data,
        "serialization_profile": "copy",
        "metadata": {
            # "profile": "",
            "environment": {},
            "deserialize": {
                "python_object": {
                    "module_type": "deserialize.file",
                    "module_config": {
                        "value_type": "file",
                        "target_profile": "python_object",
                        "serialization_profile": "copy",
                    },
                }
            },
        },
    }
    from kiara.models.values.value import SerializationResult

    serialized = SerializationResult(**serialized_data)
    return serialized

internal special

logger

Classes

DocumentationModelValueType (InternalModelValueType)

Documentation for an internal entity.

Source code in kiara/data_types/included_core_types/internal/__init__.py

class DocumentationModelValueType(InternalModelValueType):
    """Documentation for an internal entity."""

    _data_type_name = "doc"

    def parse_python_obj(self, data: Any) -> DocumentationMetadataModel:

        return DocumentationMetadataModel.create(data)

    @classmethod
    def python_class(cls) -> Type:
        return DocumentationMetadataModel

    def pretty_print_as__terminal_renderable(
        self, value: "Value", render_config: Mapping[str, Any]
    ):
        json_str = value.data.json(option=orjson.OPT_INDENT_2)
        return Syntax(json_str, "json", background_color="default")

Methods

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
DocumentationMetadataModel	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/included_core_types/internal/__init__.py

def parse_python_obj(self, data: Any) -> DocumentationMetadataModel:

    return DocumentationMetadataModel.create(data)

pretty_print_as__terminal_renderable(self, value, render_config)

Source code in kiara/data_types/included_core_types/internal/__init__.py

def pretty_print_as__terminal_renderable(
    self, value: "Value", render_config: Mapping[str, Any]
):
    json_str = value.data.json(option=orjson.OPT_INDENT_2)
    return Syntax(json_str, "json", background_color="default")

python_class() classmethod

Source code in kiara/data_types/included_core_types/internal/__init__.py

@classmethod
def python_class(cls) -> Type:
    return DocumentationMetadataModel

InternalModelTypeConfig (DataTypeConfig) pydantic-model

Source code in kiara/data_types/included_core_types/internal/__init__.py

class InternalModelTypeConfig(DataTypeConfig):

    kiara_model_id: Optional[str] = Field(
        description="The Python class backing this model (must sub-class 'KiaraModel')."
    )

Attributes

kiara_model_id: str pydantic-field

The Python class backing this model (must sub-class 'KiaraModel').

InternalModelValueType (InternalType)

A value type that is used internally.

This type should not be used by user-facing modules and/or operations.

Source code in kiara/data_types/included_core_types/internal/__init__.py

class InternalModelValueType(InternalType[KiaraModel, InternalModelTypeConfig]):
    """A value type that is used internally.

    This type should not be used by user-facing modules and/or operations.
    """

    _data_type_name = "internal_model"
    _cls_cache: Optional[Type[KiaraModel]] = PrivateAttr(default=None)

    @classmethod
    def data_type_config_class(cls) -> Type[InternalModelTypeConfig]:
        return InternalModelTypeConfig  # type: ignore

    def serialize(self, data: KiaraModel) -> Union[str, SerializedData]:

        if self.type_config.kiara_model_id is None:
            logger.debug(
                "ignore.serialize_request",
                data_type="internal_model",
                cls=data.__class__.__name__,
                reason="no model id in module config",
            )
            return NO_SERIALIZATION_MARKER

        _data = {
            "data": {
                "type": "inline-json",
                "inline_data": data.dict(),
                "codec": "json",
            },
        }

        serialized_data = {
            "data_type": self.data_type_name,
            "data_type_config": self.type_config.dict(),
            "data": _data,
            "serialization_profile": "json",
            "metadata": {
                "environment": {},
                "deserialize": {
                    "python_object": {
                        "module_type": "load.internal_model",
                        "module_config": {
                            "value_type": "internal_model",
                            "target_profile": "python_object",
                            "serialization_profile": "json",
                        },
                    }
                },
            },
        }
        from kiara.models.values.value import SerializationResult

        serialized = SerializationResult(**serialized_data)
        return serialized

    @classmethod
    def python_class(cls) -> Type:
        return KiaraModel

    @property
    def model_cls(self) -> Type[KiaraModel]:

        if self._cls_cache is not None:
            return self._cls_cache

        model_type_id = self.type_config.kiara_model_id
        assert model_type_id is not None

        model_registry = ModelRegistry.instance()

        model_cls = model_registry.get_model_cls(
            model_type_id, required_subclass=KiaraModel
        )

        self._cls_cache = model_cls
        return self._cls_cache

    def parse_python_obj(self, data: Any) -> KiaraModel:

        if isinstance(data, KiaraModel):
            return data
        elif isinstance(data, Mapping):
            return self.model_cls(**data)
        else:
            raise ValueError(
                f"Can't parse data, invalid type '{type(data)}': must be subclass of 'KiaraModel' or Mapping."
            )

    def _validate(self, value: KiaraModel) -> None:

        if not isinstance(value, KiaraModel):
            raise Exception(f"Invalid type: {type(value)}.")

    def pretty_print_as__terminal_renderable(
        self, value: "Value", render_config: Mapping[str, Any]
    ):
        json_str = value.data.json(option=orjson.OPT_INDENT_2)
        return Syntax(json_str, "json", background_color="default")

model_cls: Type[kiara.models.KiaraModel] property readonly

Methods

data_type_config_class() classmethod

Source code in kiara/data_types/included_core_types/internal/__init__.py

@classmethod
def data_type_config_class(cls) -> Type[InternalModelTypeConfig]:
    return InternalModelTypeConfig  # type: ignore

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
KiaraModel	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/included_core_types/internal/__init__.py

def parse_python_obj(self, data: Any) -> KiaraModel:

    if isinstance(data, KiaraModel):
        return data
    elif isinstance(data, Mapping):
        return self.model_cls(**data)
    else:
        raise ValueError(
            f"Can't parse data, invalid type '{type(data)}': must be subclass of 'KiaraModel' or Mapping."
        )

pretty_print_as__terminal_renderable(self, value, render_config)

Source code in kiara/data_types/included_core_types/internal/__init__.py

def pretty_print_as__terminal_renderable(
    self, value: "Value", render_config: Mapping[str, Any]
):
    json_str = value.data.json(option=orjson.OPT_INDENT_2)
    return Syntax(json_str, "json", background_color="default")

python_class() classmethod

Source code in kiara/data_types/included_core_types/internal/__init__.py

@classmethod
def python_class(cls) -> Type:
    return KiaraModel

serialize(self, data)

Source code in kiara/data_types/included_core_types/internal/__init__.py

def serialize(self, data: KiaraModel) -> Union[str, SerializedData]:

    if self.type_config.kiara_model_id is None:
        logger.debug(
            "ignore.serialize_request",
            data_type="internal_model",
            cls=data.__class__.__name__,
            reason="no model id in module config",
        )
        return NO_SERIALIZATION_MARKER

    _data = {
        "data": {
            "type": "inline-json",
            "inline_data": data.dict(),
            "codec": "json",
        },
    }

    serialized_data = {
        "data_type": self.data_type_name,
        "data_type_config": self.type_config.dict(),
        "data": _data,
        "serialization_profile": "json",
        "metadata": {
            "environment": {},
            "deserialize": {
                "python_object": {
                    "module_type": "load.internal_model",
                    "module_config": {
                        "value_type": "internal_model",
                        "target_profile": "python_object",
                        "serialization_profile": "json",
                    },
                }
            },
        },
    }
    from kiara.models.values.value import SerializationResult

    serialized = SerializationResult(**serialized_data)
    return serialized

InternalType (DataType, Generic)

'A 'marker' base data type for data types that are (mainly) used internally in kiara..

Source code in kiara/data_types/included_core_types/internal/__init__.py

class InternalType(
    DataType[TYPE_PYTHON_CLS, TYPE_CONFIG_CLS],
    Generic[TYPE_PYTHON_CLS, TYPE_CONFIG_CLS],
):
    """'A 'marker' base data type for data types that are (mainly) used internally in kiara.."""

    _data_type_name = "internal"

    @classmethod
    def python_class(cls) -> Type:
        return object

    def pretty_print_as__string(
        self, value: "Value", render_config: Mapping[str, Any]
    ) -> Any:

        data = value.data
        return str(data)

pretty_print_as__string(self, value, render_config)

Source code in kiara/data_types/included_core_types/internal/__init__.py

def pretty_print_as__string(
    self, value: "Value", render_config: Mapping[str, Any]
) -> Any:

    data = value.data
    return str(data)

python_class() classmethod

Source code in kiara/data_types/included_core_types/internal/__init__.py

@classmethod
def python_class(cls) -> Type:
    return object

TerminalRenderable (InternalType)

A list of renderable objects, used in the 'rich' Python library, to print to the terminal or in Jupyter.

Internally, the result list items can be either a string, a 'rich.console.ConsoleRenderable', or a 'rich.console.RichCast'.

Source code in kiara/data_types/included_core_types/internal/__init__.py

class TerminalRenderable(InternalType[object, DataTypeConfig]):
    """A list of renderable objects, used in the 'rich' Python library, to print to the terminal or in Jupyter.

    Internally, the result list items can be either a string, a 'rich.console.ConsoleRenderable', or a 'rich.console.RichCast'.
    """

    _data_type_name = "terminal_renderable"

    @classmethod
    def python_class(cls) -> Type:
        return object

python_class() classmethod

Source code in kiara/data_types/included_core_types/internal/__init__.py

@classmethod
def python_class(cls) -> Type:
    return object

Modules

render_value

Classes

RenderInstructionDataType (InternalType)

A value type to contain information about how to render a value in a specific render scenario.

Source code in kiara/data_types/included_core_types/internal/render_value.py

class RenderInstructionDataType(
    InternalType[RenderInstruction, RenderInstructionTypeConfig]
):
    """A value type to contain information about how to render a value in a specific render scenario."""

    _data_type_name = "render_instruction"

    def __init__(self, **type_config: Any):

        self._cls_cache: Optional[Type[RenderInstruction]] = None
        super().__init__(**type_config)

    @classmethod
    def python_class(cls) -> Type:
        return RenderInstruction

    @classmethod
    def data_type_config_class(cls) -> Type[RenderInstructionTypeConfig]:
        return RenderInstructionTypeConfig

    @property
    def model_cls(self) -> Type[RenderInstruction]:

        if self._cls_cache is not None:
            return self._cls_cache

        all_models = find_all_kiara_model_classes()
        if self.type_config.kiara_model_id not in all_models.keys():
            raise Exception(f"Invalid model id: {self.type_config.kiara_model_id}")

        model_cls = all_models[self.type_config.kiara_model_id]

        assert issubclass(model_cls, RenderInstruction)
        self._cls_cache = model_cls
        return self._cls_cache

    def parse_python_obj(self, data: Any) -> RenderInstruction:

        if data is None:
            return self.model_cls()
        elif isinstance(data, RenderInstruction):
            return data
        elif isinstance(data, Mapping):
            return self.model_cls(**data)
        else:
            raise ValueError(
                f"Can't parse data, invalid type '{type(data)}': must be subclass of 'KiaraModel' or Mapping."
            )

    def _validate(self, value: RenderInstruction) -> None:

        if not isinstance(value, RenderInstruction):
            raise Exception(f"Invalid type: {type(value)}.")

model_cls: Type[kiara.models.render_value.RenderInstruction] property readonly

Methods

data_type_config_class() classmethod

Source code in kiara/data_types/included_core_types/internal/render_value.py

@classmethod
def data_type_config_class(cls) -> Type[RenderInstructionTypeConfig]:
    return RenderInstructionTypeConfig

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
RenderInstruction	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/included_core_types/internal/render_value.py

def parse_python_obj(self, data: Any) -> RenderInstruction:

    if data is None:
        return self.model_cls()
    elif isinstance(data, RenderInstruction):
        return data
    elif isinstance(data, Mapping):
        return self.model_cls(**data)
    else:
        raise ValueError(
            f"Can't parse data, invalid type '{type(data)}': must be subclass of 'KiaraModel' or Mapping."
        )

python_class() classmethod

Source code in kiara/data_types/included_core_types/internal/render_value.py

@classmethod
def python_class(cls) -> Type:
    return RenderInstruction

RenderInstructionTypeConfig (DataTypeConfig) pydantic-model

Source code in kiara/data_types/included_core_types/internal/render_value.py

class RenderInstructionTypeConfig(DataTypeConfig):

    kiara_model_id: str = Field(
        description="The id of the model backing this render (Python class must sub-class 'RenderInstruction').",
        default="instance.render_instruction.table",
    )

Attributes

kiara_model_id: str pydantic-field

The id of the model backing this render (Python class must sub-class 'RenderInstruction').

RenderMetadataDataType (InternalType)

A value type to contain information about how to render a value in a specific render scenario.

Source code in kiara/data_types/included_core_types/internal/render_value.py

class RenderMetadataDataType(InternalType[RenderMetadata, DataTypeConfig]):
    """A value type to contain information about how to render a value in a specific render scenario."""

    _data_type_name = "render_metadata"

    def __init__(self, **type_config: Any):

        self._cls_cache: Optional[Type[RenderMetadata]] = None
        super().__init__(**type_config)

    @classmethod
    def python_class(cls) -> Type:
        return RenderMetadata

    def parse_python_obj(self, data: Any) -> RenderMetadata:

        if data is None:
            return RenderMetadata()
        elif isinstance(data, RenderMetadata):
            return data
        elif isinstance(data, Mapping):
            return RenderMetadata(**data)
        else:
            raise ValueError(
                f"Can't parse data, invalid type '{type(data)}': must be subclass of 'KiaraModel' or Mapping."
            )

    def _validate(self, value: Any) -> None:

        if not isinstance(value, RenderMetadata):
            raise Exception(f"Invalid type: {type(value)}.")

Methods

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
RenderMetadata	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/included_core_types/internal/render_value.py

def parse_python_obj(self, data: Any) -> RenderMetadata:

    if data is None:
        return RenderMetadata()
    elif isinstance(data, RenderMetadata):
        return data
    elif isinstance(data, Mapping):
        return RenderMetadata(**data)
    else:
        raise ValueError(
            f"Can't parse data, invalid type '{type(data)}': must be subclass of 'KiaraModel' or Mapping."
        )

python_class() classmethod

Source code in kiara/data_types/included_core_types/internal/render_value.py

@classmethod
def python_class(cls) -> Type:
    return RenderMetadata

serialization

Classes

PythonObjectType (InternalType)

A 'plain' Python object.

This data type is mostly used internally, for hading over data in (de-)serialization operations.

Source code in kiara/data_types/included_core_types/serialization.py

class PythonObjectType(InternalType[object, DataTypeConfig]):
    """A 'plain' Python object.

    This data type is mostly used internally, for hading over data in (de-)serialization operations.
    """

    @classmethod
    def python_class(cls) -> Type:
        return object

    def parse_python_obj(self, data: Any) -> object:
        return data

    def calculate_hash(self, data: SerializedData) -> str:
        """Calculate the hash of the value."""
        return INVALID_HASH_MARKER

    def calculate_size(self, data: SerializedData) -> int:
        return INVALID_SIZE_MARKER

    def pretty_print_as__terminal_renderable(
        self, value: Value, render_config: Mapping[str, Any]
    ):

        return str(value.data)

Methods

calculate_hash(self, data)

Calculate the hash of the value.

Source code in kiara/data_types/included_core_types/serialization.py

def calculate_hash(self, data: SerializedData) -> str:
    """Calculate the hash of the value."""
    return INVALID_HASH_MARKER

calculate_size(self, data)

Calculate the size of the value.

Source code in kiara/data_types/included_core_types/serialization.py

def calculate_size(self, data: SerializedData) -> int:
    return INVALID_SIZE_MARKER

parse_python_obj(self, data)

Parse a value into a supported python type.

This exists to make it easier to do trivial conversions (e.g. from a date string to a datetime object). If you choose to overwrite this method, make 100% sure that you don't change the meaning of the value, and try to avoid adding or removing information from the data (e.g. by changing the resolution of a date).

Parameters:

Name	Type	Description	Default
v		the value	required

Returns:

Type	Description
object	'None', if no parsing was done and the original value should be used, otherwise return the parsed Python object

Source code in kiara/data_types/included_core_types/serialization.py

def parse_python_obj(self, data: Any) -> object:
    return data

pretty_print_as__terminal_renderable(self, value, render_config)

Source code in kiara/data_types/included_core_types/serialization.py

def pretty_print_as__terminal_renderable(
    self, value: Value, render_config: Mapping[str, Any]
):

    return str(value.data)

python_class() classmethod

Source code in kiara/data_types/included_core_types/serialization.py

@classmethod
def python_class(cls) -> Type:
    return object

defaults

Attributes

ANY_TYPE_NAME

ARRAY_MODEL_CATEOGORY_ID

AUTHORS_METADATA_CATEGORY_ID

BATCH_CONFIG_TYPE_CATEGORY_ID

COLOR_LIST

CONTEXT_INFO_CATEGORY_ID

CONTEXT_METADATA_CATEOGORY_ID

DATA_TYPES_CATEGORY_ID

DATA_TYPE_CATEGORY_ID

DATA_TYPE_CLASS_CATEGORY_ID

DATA_WRAP_CATEGORY_ID

DEFAULT_ALIAS_STORE_MARKER

Name for the default context job store.

DEFAULT_CONTEXT_NAME

DEFAULT_DATA_STORE_MARKER

Name for the default context data store.

DEFAULT_EXCLUDE_DIRS

List of directory names to exclude by default when walking a folder recursively.

DEFAULT_EXCLUDE_FILES

List of file names to exclude by default when reading folders.

DEFAULT_JOB_STORE_MARKER

Name for the default context job store.

DEFAULT_NO_DESC_VALUE

DEFAULT_PIPELINE_PARENT_ID

Default parent id for pipeline objects that are not associated with a workflow.

DEFAULT_PRETTY_PRINT_CONFIG

DEFAULT_TO_JSON_CONFIG: Mapping[str, Any]

DESTINY_CATEGORY_ID

DOCUMENTATION_CATEGORY_ID

ENVIRONMENT_TYPE_CATEGORY_ID

FILE_BUNDLE_MODEL_CATEOGORY_ID

FILE_MODEL_CATEOGORY_ID

INVALID_HASH_MARKER

INVALID_SIZE_MARKER

INVALID_VALUE_NAMES

List of reserved names, inputs/outputs can't use those.

JOB_CATEGORY_ID

JOB_CONFIG_TYPE_CATEGORY_ID

JOB_LOG_CATEGORY_ID

JOB_RECORD_TYPE_CATEGORY_ID

KIARA_CONFIG_FILE_NAME

KIARA_DB_MIGRATIONS_CONFIG

KIARA_DB_MIGRATIONS_FOLDER

KIARA_DEFAULT_ROOT_NODE_ID

KIARA_HASH_FUNCTION

KIARA_MAIN_CONFIG_FILE

KIARA_MAIN_CONTEXTS_PATH

KIARA_MODULE_BASE_FOLDER

Marker to indicate the base folder for the kiara module.

KIARA_MODULE_METADATA_ATTRIBUTE

KIARA_RESOURCES_FOLDER

Default resources folder for this package.

KIARA_ROOT_TYPE_NAME

LOAD_CONFIG_DATA_TYPE_NAME

LOAD_CONFIG_PLACEHOLDER

MODULE_CONFIG_CATEGORY_ID

MODULE_CONFIG_METADATA_CATEGORY_ID

MODULE_CONFIG_SCHEMA_CATEGORY_ID

MODULE_TYPES_CATEGORY_ID

MODULE_TYPE_CATEGORY_ID

MODULE_TYPE_KEY

The key to specify the type of a module.

MODULE_TYPE_NAME_KEY

The string for the module type name in a module configuration dict.

NONE_STORE_ID

NONE_VALUE_ID

NOT_SET_VALUE_ID

NO_HASH_MARKER

Marker string to indicate no hash was calculated.

NO_MODULE_TYPE

NO_SERIALIZATION_MARKER

NO_VALUE_ID_MARKER

Marker string to indicate no value id exists.

OPERATIONS_CATEGORY_ID

OPERATION_CATEOGORY_ID

OPERATION_CONFIG_CATEOGORY_ID

OPERATION_DETAILS_CATEOGORY_ID

OPERATION_INPUTS_SCHEMA_CATEOGORY_ID

OPERATION_OUTPUTS_SCHEMA_CATEOGORY_ID

OPERATION_TYPES_CATEGORY_ID

OPERATION_TYPE_CATEGORY_ID

ORPHAN_PEDIGREE_OUTPUT_NAME

PIPELINE_CONFIG_TYPE_CATEGORY_ID

PIPELINE_PARENT_MARKER

Marker string in the pipeline structure that indicates a parent pipeline element.

PIPELINE_STEP_DETAILS_CATEGORY_ID

PIPELINE_STEP_TYPE_CATEGORY_ID

PIPELINE_STRUCTURE_TYPE_CATEGORY_ID

PIPELINE_TYPES_CATEGORY_ID

PIPELINE_TYPE_CATEGORY_ID

PYDANTIC_USE_CONSTRUCT: bool

SERIALIZED_DATA_TYPE_NAME

STEP_ID_KEY

The key to specify the step id.

STRICT_CHECKS: bool

TABLE_MODEL_CATEOGORY_ID

UNOLOADABLE_DATA_CATEGORY_ID

USER_PIPELINES_FOLDER

VALID_PIPELINE_FILE_EXTENSIONS

File extensions a kiara pipeline/workflow file can have.

VALUES_CATEGORY_ID

VALUE_CATEGORY_ID

VALUE_METADATA_CATEGORY_ID

VALUE_PEDIGREE_TYPE_CATEGORY_ID

VALUE_SCHEMA_CATEGORY_ID

VOID_KIARA_ID

kiara_app_dirs

Classes

SpecialValue (Enum)

An enumeration.

Source code in kiara/defaults.py

class SpecialValue(Enum):

    NOT_SET = "__not_set__"
    NO_VALUE = "__no_value__"

NOT_SET

NO_VALUE

doc special

Main module for code that helps with documentation auto-generation in supported projects.

Classes

FrklDocumentationPlugin (BasePlugin)

mkdocs plugin to render API documentation for a project.

To add to a project, add this to the 'plugins' section of a mkdocs config file:

- frkl-docgen:
    main_module: "module_name"

This will add an API reference navigation item to your page navigation, with auto-generated entries for every Python module in your package.

Source code in kiara/doc/__init__.py

class FrklDocumentationPlugin(BasePlugin):
    """[mkdocs](https://www.mkdocs.org/) plugin to render API documentation for a project.

    To add to a project, add this to the 'plugins' section of a mkdocs config file:

    ```yaml
    - frkl-docgen:
        main_module: "module_name"
    ```

    This will add an ``API reference`` navigation item to your page navigation, with auto-generated entries for every
    Python module in your package.
    """

    config_scheme = (("main_module", mkdocs.config.config_options.Type(str)),)

    def __init__(self):
        self._doc_paths = None
        self._dir = tempfile.TemporaryDirectory(prefix="frkl_doc_gen_")
        self._doc_files = None
        super().__init__()

    def on_files(self, files: Files, config: Config) -> Files:

        self._doc_paths = gen_pages_for_module(self.config["main_module"])
        self._doc_files = {}

        for k in sorted(self._doc_paths, key=lambda x: os.path.splitext(x)[0]):
            content = self._doc_paths[k]["content"]
            _file = File(
                k,
                src_dir=self._dir.name,
                dest_dir=config["site_dir"],
                use_directory_urls=config["use_directory_urls"],
            )

            os.makedirs(os.path.dirname(_file.abs_src_path), exist_ok=True)

            with open(_file.abs_src_path, "w") as f:
                f.write(content)

            self._doc_files[k] = _file
            files.append(_file)

        return files

    def on_page_content(self, html, page: Page, config: Config, files: Files):

        repo_url = config.get("repo_url", None)
        python_src = config.get("edit_uri", None)

        if page.file.src_path in self._doc_paths.keys():
            src_path = self._doc_paths.get(page.file.src_path)["python_src"]["rel_path"]
            rel_base = urllib.parse.urljoin(repo_url, f"{python_src}/../src/{src_path}")
            page.edit_url = rel_base

        return html

    def on_nav(self, nav: Navigation, config: Config, files: Files):

        for item in nav.items:
            if item.title and "Api reference" in item.title:
                return nav

        pages = []
        for _file in self._doc_files.values():
            pages.append(_file.page)

        section = Section(title="API reference", children=pages)
        nav.items.append(section)
        nav.pages.extend(pages)

        _add_previous_and_next_links(nav.pages)
        _add_parent_links(nav.items)

        return nav

    def on_post_build(self, config: Config):

        self._dir.cleanup()

config_scheme

on_files(self, files, config)

Source code in kiara/doc/__init__.py

def on_files(self, files: Files, config: Config) -> Files:

    self._doc_paths = gen_pages_for_module(self.config["main_module"])
    self._doc_files = {}

    for k in sorted(self._doc_paths, key=lambda x: os.path.splitext(x)[0]):
        content = self._doc_paths[k]["content"]
        _file = File(
            k,
            src_dir=self._dir.name,
            dest_dir=config["site_dir"],
            use_directory_urls=config["use_directory_urls"],
        )

        os.makedirs(os.path.dirname(_file.abs_src_path), exist_ok=True)

        with open(_file.abs_src_path, "w") as f:
            f.write(content)

        self._doc_files[k] = _file
        files.append(_file)

    return files

on_nav(self, nav, config, files)

Source code in kiara/doc/__init__.py

def on_nav(self, nav: Navigation, config: Config, files: Files):

    for item in nav.items:
        if item.title and "Api reference" in item.title:
            return nav

    pages = []
    for _file in self._doc_files.values():
        pages.append(_file.page)

    section = Section(title="API reference", children=pages)
    nav.items.append(section)
    nav.pages.extend(pages)

    _add_previous_and_next_links(nav.pages)
    _add_parent_links(nav.items)

    return nav

on_page_content(self, html, page, config, files)

Source code in kiara/doc/__init__.py

def on_page_content(self, html, page: Page, config: Config, files: Files):

    repo_url = config.get("repo_url", None)
    python_src = config.get("edit_uri", None)

    if page.file.src_path in self._doc_paths.keys():
        src_path = self._doc_paths.get(page.file.src_path)["python_src"]["rel_path"]
        rel_base = urllib.parse.urljoin(repo_url, f"{python_src}/../src/{src_path}")
        page.edit_url = rel_base

    return html

on_post_build(self, config)

Source code in kiara/doc/__init__.py

def on_post_build(self, config: Config):

    self._dir.cleanup()

Modules

gen_info_pages

generate_detail_pages(context_info, sub_path='info', add_summary_page=False)

Source code in kiara/doc/gen_info_pages.py

def generate_detail_pages(
    context_info: KiaraContextInfo,
    sub_path: str = "info",
    add_summary_page: bool = False,
):

    pages = {}
    summary = []

    all_info = context_info.get_all_info(skip_empty_types=True)

    for item_type, items_info in all_info.items():
        summary.append(f"* [{item_type}]({item_type}.md)")
        path = render_item_listing(
            item_type=item_type, items=items_info, sub_path=sub_path
        )
        pages[item_type] = path

    if summary:
        if add_summary_page:
            summary.insert(0, "* [Summary](index.md)")

        with mkdocs_gen_files.open(f"{sub_path}/SUMMARY.md", "w") as f:
            f.write("\n".join(summary))

    return pages

get_jina_env()

Source code in kiara/doc/gen_info_pages.py

def get_jina_env():

    global _jinja_env
    if _jinja_env is None:
        from jinja2 import Environment, FileSystemLoader

        _jinja_env = Environment(
            loader=FileSystemLoader(
                os.path.join(KIARA_RESOURCES_FOLDER, "templates", "doc_gen"),
                encoding="utf8",
            )
        )
    return _jinja_env

render_item_listing(item_type, items, sub_path='info')

Source code in kiara/doc/gen_info_pages.py

def render_item_listing(item_type: str, items: InfoModelGroup, sub_path: str = "info"):

    list_template = get_jina_env().get_template("info_listing.j2")

    render_args = {"items": items.get_type_infos(), "item_type": item_type}
    rendered = list_template.render(**render_args)

    path = f"{sub_path}/{item_type}.md"
    with mkdocs_gen_files.open(path, "w") as f:
        f.write(rendered)

    return path

generate_api_doc

Functions

gen_pages_for_module(module, prefix='api_reference')

Generate modules for a set of modules (using the mkdocstring package.

Source code in kiara/doc/generate_api_doc.py

def gen_pages_for_module(
    module: typing.Union[str, ModuleType], prefix: str = "api_reference"
):
    """Generate modules for a set of modules (using the [mkdocstring](https://github.com/mkdocstrings/mkdocstrings) package."""

    result = {}
    modules_info = get_source_tree(module)
    for module_name, path in modules_info.items():

        page_name = module_name

        if page_name.endswith("__init__"):
            page_name = page_name[0:-9]
        if page_name.endswith("._frkl"):
            continue

        doc_path = f"{prefix}{os.path.sep}{page_name}.md"
        p = Path("..", path["abs_path"])
        if not p.read_text().strip():
            continue

        main_module = path["main_module"]
        if page_name == main_module:
            title = page_name
        else:
            title = page_name.replace(f"{main_module}.", "➜ ")

        result[doc_path] = {
            "python_src": path,
            "content": f"---\ntitle: {title}\n---\n# {page_name}\n\n::: {module_name}",
        }

    return result

get_source_tree(module)

Find all python source files for a module.

Source code in kiara/doc/generate_api_doc.py

def get_source_tree(module: typing.Union[str, ModuleType]):
    """Find all python source files for a module."""

    if isinstance(module, str):
        module = importlib.import_module(module)

    if not isinstance(module, ModuleType):
        raise TypeError(
            f"Invalid type '{type(module)}', input needs to be a string or module."
        )

    module_file = module.__file__
    assert module_file is not None
    module_root = os.path.dirname(module_file)
    module_name = module.__name__

    src = {}

    for path in Path(module_root).glob("**/*.py"):

        rel = os.path.relpath(path, module_root)
        mod_name = f"{module_name}.{rel[0:-3].replace(os.path.sep, '.')}"
        rel_path = f"{module_name}{os.path.sep}{rel}"
        src[mod_name] = {
            "rel_path": rel_path,
            "abs_path": path,
            "main_module": module_name,
        }

    return src

mkdocs_macros_cli

KIARA_DOC_BUILD_CACHE_DIR

os_env_vars

Functions

define_env(env)

Helper macros for Python project documentation.

Currently, those macros are available (check the source code for more details):

cli

Execute a command on the command-line, capture the output and return it to be used in a documentation page.

inline_file_as_codeblock

Read an external file, and return its content as a markdown code block.

Source code in kiara/doc/mkdocs_macros_cli.py

def define_env(env):
    """
    Helper macros for Python project documentation.

    Currently, those macros are available (check the source code for more details):

    ## ``cli``

    Execute a command on the command-line, capture the output and return it to be used in a documentation page.

    ## ``inline_file_as_codeblock``

    Read an external file, and return its content as a markdown code block.
    """

    # env.variables["baz"] = "John Doe"

    @env.macro
    def cli(
        *command,
        print_command: bool = True,
        code_block: bool = True,
        split_command_and_output: bool = True,
        max_height: Optional[int] = None,
        cache_key: Optional[str] = None,
        extra_env: Optional[Dict[str, str]] = None,
        fake_command: Optional[str] = None,
        fail_ok: bool = False,
    ):
        """Execute the provided command, save the output and return it to be used in documentation modules."""

        hashes = DeepHash(command)
        hash_str = hashes[command]
        hashes_env = DeepHash(extra_env)
        hashes_env_str = hashes_env[extra_env]

        hash_str = hash_str + "_" + hashes_env_str
        if cache_key:
            hash_str = hash_str + "_" + cache_key

        cache_file: Path = Path(os.path.join(KIARA_DOC_BUILD_CACHE_DIR, str(hash_str)))
        failed_cache_file: Path = Path(
            os.path.join(KIARA_DOC_BUILD_CACHE_DIR, f"{hash_str}.failed")
        )
        cache_info_file: Path = Path(
            os.path.join(KIARA_DOC_BUILD_CACHE_DIR), f"{hash_str}.command"
        )

        _run_env = dict(os_env_vars)
        if extra_env:
            _run_env.update(extra_env)

        if cache_file.is_file():
            stdout_str = cache_file.read_text()
        else:
            start = timer()

            cache_info = {
                "command": command,
                "extra_env": extra_env,
                "cmd_hash": hash_str,
                "cache_key": cache_key,
                "fail_ok": fail_ok,
                "started": start,
            }

            print(f"RUNNING: {' '.join(command)}")
            p = Popen(command, stdout=PIPE, stderr=PIPE, env=_run_env)
            stdout, stderr = p.communicate()

            stdout_str = stdout.decode("utf-8")
            stderr_str = stderr.decode("utf-8")
            print("stdout:")
            print(stdout_str)
            print("stderr:")
            print(stderr_str)

            cache_info["exit_code"] = p.returncode

            end = timer()
            if p.returncode == 0:

                # result = subprocess.check_output(command, env=_run_env)

                # stdout = result.decode()
                cache_file.write_bytes(stdout)
                cache_info["size"] = len(stdout)
                cache_info["duration"] = end - start
                cache_info["success"] = True
                cache_info["output_file"] = cache_file.as_posix()
                cache_info_file.write_bytes(orjson.dumps(cache_info))

                if failed_cache_file.exists():
                    failed_cache_file.unlink()
            else:

                cache_info["duration"] = end - start

                if fail_ok:
                    cache_info["size"] = len(stdout)
                    cache_info["success"] = True
                    cache_file.write_bytes(stdout)
                    cache_info["output_file"] = cache_file.as_posix()
                    cache_info_file.write_bytes(orjson.dumps(cache_info))
                    if failed_cache_file.exists():
                        failed_cache_file.unlink()
                else:
                    cache_info["size"] = len(stdout)
                    cache_info["success"] = False
                    failed_cache_file.write_bytes(stdout)
                    cache_info["output_file"] = failed_cache_file.as_posix()
                    cache_info_file.write_bytes(orjson.dumps(cache_info))
                    # stdout = f"Error: {e}\n\nStdout: {e.stdout}\n\nStderr: {e.stderr}"
                    # cache_info["size"] = len(stdout)
                    # cache_info["success"] = False
                    # print("stdout:")
                    # print(e.stdout)
                    # print("stderr:")
                    # print(e.stderr)
                    # failed_cache_file.write_text(stdout)
                    # cache_info["output_file"] = failed_cache_file.as_posix()
                    # cache_info_file.write_bytes(orjson.dumps(cache_info))
                    if os.getenv("FAIL_DOC_BUILD_ON_ERROR") == "true":
                        sys.exit(1)

        if fake_command:
            command_str = fake_command
        else:
            command_str = " ".join(command)

        if split_command_and_output and print_command:
            _c = f"\n``` console\n{command_str}\n```\n"
            _output = "``` console\n" + stdout_str + "\n```\n"
            if max_height is not None and max_height > 0:
                _output = f"<div style='max-height:{max_height}px;overflow:auto'>\n{_output}\n</div>"
            _stdout = _c + _output
        else:
            if print_command:
                _stdout = f"> {command_str}\n{stdout_str}"
            if code_block:
                _stdout = "``` console\n" + _stdout + "\n```\n"

            if max_height is not None and max_height > 0:
                _stdout = f"<div style='max-height:{max_height}px;overflow:auto'>\n{_stdout}\n</div>"

        return _stdout

    @env.macro
    def inline_file_as_codeblock(path, format: str = ""):
        """Import external file and return its content as a markdown code block."""

        f = Path(path)
        return f"```{format}\n{f.read_text()}\n```"

mkdocs_macros_kiara

kiara_obj

yaml

Functions

define_env(env)

This is the hook for defining variables, macros and filters

• variables: the dictionary that contains the environment variables
• macro: a decorator function, to declare a macro.

Source code in kiara/doc/mkdocs_macros_kiara.py

def define_env(env):
    """
    This is the hook for defining variables, macros and filters

    - variables: the dictionary that contains the environment variables
    - macro: a decorator function, to declare a macro.
    """

    # env.variables["baz"] = "John Doe"

    @env.macro
    def get_schema_for_model(model_class: Union[str, Type[BaseModel]]):

        if isinstance(model_class, str):
            _class: Type[BaseModel] = locate(model_class)  # type: ignore
        else:
            _class = model_class

        schema_json = _class.schema_json(indent=2)

        return schema_json

    @env.macro
    def get_src_of_object(obj: Union[str, Any]):

        try:
            if isinstance(obj, str):
                _obj: Type[BaseModel] = locate(obj)  # type: ignore
            else:
                _obj = obj

            src = inspect.getsource(_obj)
            return src
        except Exception as e:
            return f"Can't render object source: {str(e)}"

    @env.macro
    def get_context_info() -> KiaraContextInfo:

        return builtins.plugin_package_context_info  # type: ignore

    # @env.macro
    # def get_module_info(module_type: str):
    #
    #     try:
    #
    #         m_cls = Kiara.instance().module_registry.get_module_class(module_type)
    #         info = KiaraModuleTypeInfo.from_module_class(m_cls)
    #
    #         from rich.console import Console
    #
    #         console = Console(record=True)
    #         console.print(info)
    #
    #         html = console.export_text()
    #         return html
    #     except Exception as e:
    #         return f"Can't render module info: {str(e)}"
    #
    # @env.macro
    # def get_info_item_list_for_category(
    #     category: str, limit_to_package: typing.Optional[str] = None
    # ) -> typing.Dict[str, KiaraInfoModel]:
    #     return _get_info_item_list_for_category(
    #         category=category, limit_to_package=limit_to_package
    #     )
    #
    # def _get_info_item_list_for_category(
    #     category: str, limit_to_package: typing.Optional[str] = None
    # ) -> typing.Dict[str, KiaraInfoModel]:
    #
    #     infos = kiara_context.find_subcomponents(category=category)
    #
    #     if limit_to_package:
    #         temp = {}
    #         for n_id, obj in infos.items():
    #             if obj.context.labels.get("package", None) == limit_to_package:
    #                 temp[n_id] = obj
    #         infos = temp
    #
    #     docs = {}
    #     for n_id, obj in infos.items():
    #         docs[obj.get_id()] = obj.documentation.description
    #
    #     return docs
    #
    # @env.macro
    # def get_info_for_categories(
    #     *categories: str, limit_to_package: typing.Optional[str] = None
    # ):
    #
    #     TITLE_MAP = {
    #         "metadata.module": "Modules",
    #         "metadata.pipeline": "Pipelines",
    #         "metadata.type": "Value data_types",
    #         "metadata.operation_type": "Operation data_types",
    #     }
    #     result = {}
    #     for cat in categories:
    #         infos = _get_info_item_list_for_category(
    #             cat, limit_to_package=limit_to_package
    #         )
    #         if infos:
    #             result[cat] = {"items": infos, "title": TITLE_MAP[cat]}
    #
    #     return result
    #
    # @env.macro
    # def get_module_list_for_package(
    #     package_name: str,
    #     include_core_modules: bool = True,
    #     include_pipelines: bool = True,
    # ):
    #
    #     modules = kiara_obj.module_registry.find_modules_for_package(
    #         package_name,
    #         include_core_modules=include_core_modules,
    #         include_pipelines=include_pipelines,
    #     )
    #
    #     result = []
    #     for name, info in modules.items():
    #         type_md = info.get_type_metadata()
    #         result.append(
    #             f"[``{name}``][kiara_info.modules.{name}]: {type_md.documentation.description}"
    #         )
    #
    #     return result
    #
    # @env.macro
    # def get_data_types_for_package(package_name: str):
    #
    #     data_types = kiara_obj.type_registry.find_data_type_classes_for_package(
    #         package_name
    #     )
    #     result = []
    #     for name, info in data_types.items():
    #         type_md = info.get_type_metadata()
    #         result.append(f"  - ``{name}``: {type_md.documentation.description}")
    #
    #     return "\n".join(result)
    #
    # @env.macro
    # def get_metadata_models_for_package(package_name: str):
    #
    #     metadata_schemas = kiara_obj.metadata_mgmt.find_all_models_for_package(
    #         package_name
    #     )
    #     result = []
    #     for name, info in metadata_schemas.items():
    #         type_md = info.get_type_metadata()
    #         result.append(f"  - ``{name}``: {type_md.documentation.description}")
    #
    #     return "\n".join(result)
    #
    # @env.macro
    # def get_kiara_context() -> KiaraContext:
    #     return kiara_context

mkdocstrings special

Modules

collector

logger

Classes

KiaraCollector (BaseCollector)

The class responsible for loading Jinja templates and rendering them. It defines some configuration options, implements the render method, and overrides the update_env method of the [BaseRenderer class][mkdocstrings.handlers.base.BaseRenderer].

Source code in kiara/doc/mkdocstrings/collector.py

class KiaraCollector(BaseCollector):
    """The class responsible for loading Jinja templates and rendering them.
    It defines some configuration options, implements the `render` method,
    and overrides the `update_env` method of the [`BaseRenderer` class][mkdocstrings.handlers.base.BaseRenderer].
    """

    default_config: dict = {"docstring_style": "google", "docstring_options": {}}
    """The default selection options.
    Option | Type | Description | Default
    ------ | ---- | ----------- | -------
    **`docstring_style`** | `"google" | "numpy" | "sphinx" | None` | The docstring style to use. | `"google"`
    **`docstring_options`** | `dict[str, Any]` | The options for the docstring parser. | `{}`
    """

    fallback_config: dict = {"fallback": True}

    def __init__(self) -> None:
        """Initialize the collector."""

        self._kiara: Kiara = Kiara.instance()

    def collect(self, identifier: str, config: dict) -> CollectorItem:  # noqa: WPS231
        """Collect the documentation tree given an identifier and selection options.
        Arguments:
            identifier: The dotted-path of a Python object available in the Python path.
            config: Selection options, used to alter the data collection done by `pytkdocs`.
        Raises:
            CollectionError: When there was a problem collecting the object documentation.
        Returns:
            The collected object-tree.
        """

        tokens = identifier.split(".")

        if tokens[0] != "kiara_info":
            return None

        item_type = tokens[1]
        item_id = ".".join(tokens[2:])
        if not item_id:
            raise CollectionError(f"Invalid id: {identifier}")

        ctx: KiaraContextInfo = builtins.plugin_package_context_info  # type: ignore
        try:
            item: ItemInfo = ctx.get_info(item_type=item_type, item_id=item_id)
        except Exception:
            import traceback

            traceback.print_exc()
            raise CollectionError(f"Invalid id: {identifier}")

        return {"obj": item, "identifier": identifier}

Attributes

default_config: dict

The default selection options. Option | Type | Description | Default ------ | ---- | ----------- | ------- docstring_style | "google" | "numpy" | "sphinx" | None | The docstring style to use. | "google" docstring_options | dict[str, Any] | The options for the docstring parser. | {}

fallback_config: dict

Methods

collect(self, identifier, config)

Collect the documentation tree given an identifier and selection options.

Parameters:

Name	Type	Description	Default
identifier	str	The dotted-path of a Python object available in the Python path.	required
config	dict	Selection options, used to alter the data collection done by pytkdocs.	required

Exceptions:

Type	Description
CollectionError	When there was a problem collecting the object documentation.

Returns:

Type	Description
CollectorItem	The collected object-tree.

Source code in kiara/doc/mkdocstrings/collector.py

def collect(self, identifier: str, config: dict) -> CollectorItem:  # noqa: WPS231
    """Collect the documentation tree given an identifier and selection options.
    Arguments:
        identifier: The dotted-path of a Python object available in the Python path.
        config: Selection options, used to alter the data collection done by `pytkdocs`.
    Raises:
        CollectionError: When there was a problem collecting the object documentation.
    Returns:
        The collected object-tree.
    """

    tokens = identifier.split(".")

    if tokens[0] != "kiara_info":
        return None

    item_type = tokens[1]
    item_id = ".".join(tokens[2:])
    if not item_id:
        raise CollectionError(f"Invalid id: {identifier}")

    ctx: KiaraContextInfo = builtins.plugin_package_context_info  # type: ignore
    try:
        item: ItemInfo = ctx.get_info(item_type=item_type, item_id=item_id)
    except Exception:
        import traceback

        traceback.print_exc()
        raise CollectionError(f"Invalid id: {identifier}")

    return {"obj": item, "identifier": identifier}

handler

Classes

KiaraHandler (BaseHandler)

The kiara handler class.

Attributes:

Name	Type	Description
domain	str	The cross-documentation domain/language for this handler.
enable_inventory	bool	Whether this handler is interested in enabling the creation of the objects.inv Sphinx inventory file.

Source code in kiara/doc/mkdocstrings/handler.py

class KiaraHandler(BaseHandler):
    """The kiara handler class.
    Attributes:
        domain: The cross-documentation domain/language for this handler.
        enable_inventory: Whether this handler is interested in enabling the creation
            of the `objects.inv` Sphinx inventory file.
    """

    domain: str = "kiara"
    enable_inventory: bool = True

    # load_inventory = staticmethod(inventory.list_object_urls)
    #
    # @classmethod
    # def load_inventory(
    #     cls,
    #     in_file: typing.BinaryIO,
    #     url: str,
    #     base_url: typing.Optional[str] = None,
    #     **kwargs: typing.Any,
    # ) -> typing.Iterator[typing.Tuple[str, str]]:
    #     """Yield items and their URLs from an inventory file streamed from `in_file`.
    #     This implements mkdocstrings' `load_inventory` "protocol" (see plugin.py).
    #     Arguments:
    #         in_file: The binary file-like object to read the inventory from.
    #         url: The URL that this file is being streamed from (used to guess `base_url`).
    #         base_url: The URL that this inventory's sub-paths are relative to.
    #         **kwargs: Ignore additional arguments passed from the config.
    #     Yields:
    #         Tuples of (item identifier, item URL).
    #     """
    #
    #     print("XXXXXXXXXXXXXXXXXXXXXXXXXXXX")
    #
    #     if base_url is None:
    #         base_url = posixpath.dirname(url)
    #
    #     for item in Inventory.parse_sphinx(
    #         in_file, domain_filter=("py",)
    #     ).values():  # noqa: WPS526
    #         yield item.name, posixpath.join(base_url, item.uri)

domain: str

enable_inventory: bool

Functions

get_handler(theme, custom_templates=None, **config)

Simply return an instance of PythonHandler.

Parameters:

Name	Type	Description	Default
theme	str	The theme to use when rendering contents.	required
custom_templates	Optional[str]	Directory containing custom templates.	None
**config	Any	Configuration passed to the handler.	{}

Returns:

Type	Description
KiaraHandler	An instance of PythonHandler.

Source code in kiara/doc/mkdocstrings/handler.py

def get_handler(
    theme: str,  # noqa: W0613 (unused argument config)
    custom_templates: typing.Optional[str] = None,
    **config: typing.Any,
) -> KiaraHandler:
    """Simply return an instance of `PythonHandler`.
    Arguments:
        theme: The theme to use when rendering contents.
        custom_templates: Directory containing custom templates.
        **config: Configuration passed to the handler.
    Returns:
        An instance of `PythonHandler`.
    """

    if custom_templates is not None:
        raise Exception("Custom templates are not supported for the kiara renderer.")

    custom_templates = os.path.join(
        KIARA_RESOURCES_FOLDER, "templates", "info_templates"
    )

    return KiaraHandler(
        collector=KiaraCollector(),
        renderer=KiaraInfoRenderer("kiara", theme, custom_templates),
    )

renderer

Classes

AliasResolutionError

Source code in kiara/doc/mkdocstrings/renderer.py

class AliasResolutionError:
    pass

KiaraInfoRenderer (BaseRenderer)

Source code in kiara/doc/mkdocstrings/renderer.py

class KiaraInfoRenderer(BaseRenderer):

    default_config: dict = {}

    def get_anchors(
        self, data: CollectorItem
    ) -> typing.List[str]:  # noqa: D102 (ignore missing docstring)

        if data is None:
            return list()

        return list([data["identifier"], data["kiara_id"], data["obj"].get_id()])

    def render(self, data: typing.Dict[str, typing.Any], config: dict) -> str:

        # final_config = ChainMap(config, self.default_config)

        obj = data["obj"]
        html = obj.create_html()
        return html

default_config: dict

Methods

get_anchors(self, data)

Return the possible identifiers (HTML anchors) for a collected item.

Parameters:

Name	Type	Description	Default
data	Any	The collected data.	required

Returns:

Type	Description
List[str]	The HTML anchors (without '#'), or an empty tuple if this item doesn't have an anchor.

Source code in kiara/doc/mkdocstrings/renderer.py

def get_anchors(
    self, data: CollectorItem
) -> typing.List[str]:  # noqa: D102 (ignore missing docstring)

    if data is None:
        return list()

    return list([data["identifier"], data["kiara_id"], data["obj"].get_id()])

render(self, data, config)

Render a template using provided data and configuration options.

Parameters:

Name	Type	Description	Default
data	Dict[str, Any]	The collected data to render.	required
config	dict	The rendering options.	required

Returns:

Type	Description
str	The rendered template as HTML.

Source code in kiara/doc/mkdocstrings/renderer.py

def render(self, data: typing.Dict[str, typing.Any], config: dict) -> str:

    # final_config = ChainMap(config, self.default_config)

    obj = data["obj"]
    html = obj.create_html()
    return html

exceptions

FailedJobException (Exception)

Source code in kiara/exceptions.py

class FailedJobException(Exception):
    def __init__(self, job: "ActiveJob", msg: Optional[str] = None):

        self.job: ActiveJob = job
        if msg is None:
            msg = "Job failed."
        super().__init__(msg)

InvalidValuesException (Exception)

Source code in kiara/exceptions.py

class InvalidValuesException(Exception):
    def __init__(
        self,
        msg: Union[None, str, Exception] = None,
        invalid_values: Mapping[str, str] = None,
    ):

        if invalid_values is None:
            invalid_values = {}

        self.invalid_inputs: Mapping[str, str] = invalid_values

        if msg is None:
            if not self.invalid_inputs:
                _msg = "Invalid values. No details available."
            else:
                msg_parts = []
                for k, v in invalid_values.items():
                    msg_parts.append(f"{k}: {v}")
                _msg = f"Invalid values: {', '.join(msg_parts)}"
        elif isinstance(msg, Exception):
            self._parent: Optional[Exception] = msg
            _msg = str(msg)
        else:
            self._parent = None
            _msg = msg

        super().__init__(_msg)

    def create_renderable(self, **config: Any) -> Table:

        table = Table(box=box.SIMPLE, show_header=True)

        table.add_column("field name", style="i")
        table.add_column("[red]error[/red]")

        for field_name, error in self.invalid_inputs.items():

            row: List[RenderableType] = [field_name]
            row.append(error)
            table.add_row(*row)

        return table

create_renderable(self, **config)

Source code in kiara/exceptions.py

def create_renderable(self, **config: Any) -> Table:

    table = Table(box=box.SIMPLE, show_header=True)

    table.add_column("field name", style="i")
    table.add_column("[red]error[/red]")

    for field_name, error in self.invalid_inputs.items():

        row: List[RenderableType] = [field_name]
        row.append(error)
        table.add_row(*row)

    return table

JobConfigException (Exception)

Source code in kiara/exceptions.py

class JobConfigException(Exception):
    def __init__(
        self,
        msg: Union[str, Exception],
        manifest: "Manifest",
        inputs: Mapping[str, Any],
    ):

        self._manifest: Manifest = manifest
        self._inputs: Mapping[str, Any] = inputs

        if isinstance(msg, Exception):
            self._parent: Optional[Exception] = msg
            _msg = str(msg)
        else:
            self._parent = None
            _msg = msg

        super().__init__(_msg)

    @property
    def manifest(self) -> "Manifest":
        return self._manifest

    @property
    def inputs(self) -> Mapping[str, Any]:
        return self._inputs

inputs: Mapping[str, Any] property readonly

manifest: Manifest property readonly

KiaraException (Exception)

Source code in kiara/exceptions.py

class KiaraException(Exception):
    pass

KiaraModuleConfigException (Exception)

Source code in kiara/exceptions.py

class KiaraModuleConfigException(Exception):
    def __init__(
        self,
        msg: str,
        module_cls: Type["KiaraModule"],
        config: Mapping[str, Any],
        parent: Optional[Exception] = None,
    ):

        self._module_cls = module_cls
        self._config = config

        self._parent: Optional[Exception] = parent

        if not msg.endswith("."):
            _msg = msg + "."
        else:
            _msg = msg

        super().__init__(_msg)

KiaraProcessingException (Exception)

Source code in kiara/exceptions.py

class KiaraProcessingException(Exception):
    def __init__(
        self,
        msg: Union[str, Exception],
        module: Optional["KiaraModule"] = None,
        inputs: Optional[Mapping[str, "Value"]] = None,
    ):
        self._module: Optional["KiaraModule"] = module
        self._inputs: Optional[Mapping[str, Value]] = inputs
        if isinstance(msg, Exception):
            self._parent: Optional[Exception] = msg
            _msg = str(msg)
        else:
            self._parent = None
            _msg = msg
        super().__init__(_msg)

    @property
    def module(self) -> "KiaraModule":
        return self._module  # type: ignore

    @property
    def inputs(self) -> Mapping[str, "Value"]:
        return self._inputs  # type: ignore

    @property
    def parent_exception(self) -> Optional[Exception]:
        return self._parent

inputs: Mapping[str, Value] property readonly

module: KiaraModule property readonly

parent_exception: Optional[Exception] property readonly

KiaraValueException (Exception)

Source code in kiara/exceptions.py

class KiaraValueException(Exception):
    def __init__(
        self,
        data_type: Type["DataType"],
        value_data: Any,
        exception: Exception,
    ):
        self._data_type: Type["DataType"] = data_type
        self._value_data: Any = value_data
        self._exception: Exception = exception

        exc_msg = str(self._exception)
        if not exc_msg:
            exc_msg = "no details available"

        super().__init__(f"Invalid value of type '{data_type._data_type_name}': {exc_msg}")  # type: ignore

NoSuchExecutionTargetException (Exception)

Source code in kiara/exceptions.py

class NoSuchExecutionTargetException(Exception):
    def __init__(
        self,
        selected_target: str,
        available_targets: Iterable[str],
        msg: Optional[str] = None,
    ):

        if msg is None:
            msg = f"Specified run target '{selected_target}' is an operation, additional module configuration is not allowed."

        self.avaliable_targets: Iterable[str] = available_targets
        super().__init__(msg)

NoSuchValueAliasException (NoSuchValueException)

Source code in kiara/exceptions.py

class NoSuchValueAliasException(NoSuchValueException):
    def __init__(self, alias: str, msg: Optional[str] = None):
        self.value_id: uuid.UUID
        if not msg:
            msg = f"No value with alias: {alias}."
        super().__init__(msg)

NoSuchValueException (Exception)

Source code in kiara/exceptions.py

class NoSuchValueException(Exception):

    pass

NoSuchValueIdException (NoSuchValueException)

Source code in kiara/exceptions.py

class NoSuchValueIdException(NoSuchValueException):
    def __init__(self, value_id: uuid.UUID, msg: Optional[str] = None):
        self.value_id: uuid.UUID
        if not msg:
            msg = f"No value with id: {value_id}."
        super().__init__(msg)

ValueTypeConfigException (Exception)

Source code in kiara/exceptions.py

class ValueTypeConfigException(Exception):
    def __init__(
        self,
        msg: str,
        type_cls: Type["DataType"],
        config: Mapping[str, Any],
        parent: Optional[Exception] = None,
    ):

        self._type_cls = type_cls
        self._config = config

        self._parent: Optional[Exception] = parent

        if not msg.endswith("."):
            _msg = msg + "."
        else:
            _msg = msg

        super().__init__(_msg)

interfaces special

Implementation of interfaces for Kiara.

Functions

get_console()

Get a global Console instance.

Returns:

Type	Description
Console	A console instance.

Source code in kiara/interfaces/__init__.py

def get_console() -> Console:
    """Get a global Console instance.

    Returns:
        Console: A console instance.
    """
    global _console
    if _console is None or True:
        console_width = os.environ.get("CONSOLE_WIDTH", None)
        width = None

        if console_width:
            try:
                width = int(console_width)
            except Exception:
                pass

        _console = Console(width=width)

    return _console

Modules

cli special

A command-line interface for Kiara.

Modules

context special

commands

data special

Modules

commands

Data-related sub-commands for the cli.

logger

yaml

dev special

commands

module special

Modules

commands

Module related subcommands for the cli.

operation special

commands

pipeline special

Modules

commands

Pipeline-related subcommands for the cli.

get_pipeline_config(kiara_obj, pipeline_name_or_path)

Source code in kiara/interfaces/cli/pipeline/commands.py

def get_pipeline_config(kiara_obj: Kiara, pipeline_name_or_path: str) -> PipelineConfig:

    if os.path.isfile(pipeline_name_or_path):
        pc = PipelineConfig.from_file(pipeline_name_or_path, kiara=kiara_obj)
    else:
        operation: Operation = kiara_obj.operation_registry.get_operation(
            pipeline_name_or_path
        )
        pipeline_module: PipelineModule = operation.module  # type: ignore

        if not pipeline_module.is_pipeline():
            print()
            print(
                f"Specified operation id exists, but is not a pipeline: {pipeline_name_or_path}."
            )
            sys.exit(1)

        pc = pipeline_module.config

    return pc

run

The 'run' subcommand for the cli.

service special

commands

type special

Modules

commands

Type-related subcommands for the cli.

python_api special

logger

Classes

StoreValueResult (BaseModel) pydantic-model

Source code in kiara/interfaces/python_api/__init__.py

class StoreValueResult(BaseModel):

    value: Value = Field(description="The stored value.")
    aliases: List[str] = Field(
        description="The aliases that where assigned to the value when stored."
    )
    error: Optional[str] = Field(
        description="An error that occured while trying to store."
    )

Attributes

aliases: List[str] pydantic-field required

The aliases that where assigned to the value when stored.

error: str pydantic-field

An error that occured while trying to store.

value: Value pydantic-field required

The stored value.

StoreValuesResult (BaseModel) pydantic-model

Source code in kiara/interfaces/python_api/__init__.py

class StoreValuesResult(BaseModel):

    __root__: Dict[str, StoreValueResult]

    def create_renderable(self, **config: Any) -> RenderableType:

        table = Table(show_header=True, show_lines=False, box=box.SIMPLE)
        table.add_column("field", style="b")
        table.add_column("data type", style="i")
        table.add_column("stored id", style="i")
        table.add_column("alias(es)")

        for field_name, value_result in self.__root__.items():
            row = [
                field_name,
                str(value_result.value.value_schema.type),
                str(value_result.value.value_id),
            ]
            if value_result.aliases:
                row.append(", ".join(value_result.aliases))
            else:
                row.append("")
            table.add_row(*row)

        return table

    def __len__(self):
        return len(self.__root__)

create_renderable(self, **config)

Source code in kiara/interfaces/python_api/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:

    table = Table(show_header=True, show_lines=False, box=box.SIMPLE)
    table.add_column("field", style="b")
    table.add_column("data type", style="i")
    table.add_column("stored id", style="i")
    table.add_column("alias(es)")

    for field_name, value_result in self.__root__.items():
        row = [
            field_name,
            str(value_result.value.value_schema.type),
            str(value_result.value.value_id),
        ]
        if value_result.aliases:
            row.append(", ".join(value_result.aliases))
        else:
            row.append("")
        table.add_row(*row)

    return table

Modules

batch

Classes

BatchOperation (BaseModel) pydantic-model

Source code in kiara/interfaces/python_api/batch.py

class BatchOperation(BaseModel):
    class Config:
        validate_assignment = True

    @classmethod
    def from_file(
        cls,
        path: str,
        kiara: Optional["Kiara"] = None,
    ):

        data = get_data_from_file(path)
        pipeline_id = data.get("pipeline_name", None)
        if pipeline_id is None:
            name = os.path.basename(path)
            if name.endswith(".json"):
                name = name[0:-5]
            elif name.endswith(".yaml"):
                name = name[0:-5]
            data["pipeline_name"] = name

        return cls.from_config(data=data, kiara=kiara)

    @classmethod
    def from_config(
        cls,
        data: Mapping[str, Any],
        kiara: Optional["Kiara"],
    ):

        data = dict(data)
        inputs = data.pop("inputs", {})
        save = data.pop("save", False)
        pipeline_id = data.pop("pipeline_name", None)
        if pipeline_id is None:
            pipeline_id = str(uuid.uuid4())

        if kiara is None:
            kiara = Kiara.instance()

        pipeline_config = PipelineConfig.from_config(
            pipeline_name=pipeline_id, data=data, kiara=kiara
        )

        result = cls(pipeline_config=pipeline_config, inputs=inputs, save=save)
        result._kiara = kiara
        return result

    alias: str = Field(description="The batch name/alias.")
    pipeline_config: PipelineConfig = Field(
        description="The configuration of the underlying pipeline."
    )
    inputs: Dict[str, Any] = Field(
        description="The (base) inputs to use. Can be augmented before running the operation."
    )

    save_defaults: Dict[str, List[str]] = Field(
        description="Configuration which values to save, under which alias(es).",
        default_factory=dict,
    )

    _kiara: Kiara = PrivateAttr(default=None)

    @root_validator(pre=True)
    def add_alias(cls, values):

        if not values.get("alias", None):
            pc = values.get("pipeline_config", None)
            if not pc:
                raise ValueError("No pipeline config provided.")
            if isinstance(pc, PipelineConfig):
                alias = pc.pipeline_name
            else:
                alias = pc.get("pipeline_name", None)
            values["alias"] = alias

        return values

    @validator("save_defaults", always=True, pre=True)
    def validate_save(cls, save, values):

        alias = values["alias"]
        pipeline_config = values["pipeline_config"]
        return cls.create_save_aliases(
            save=save, alias=alias, pipeline_config=pipeline_config
        )

    @classmethod
    def create_save_aliases(
        cls,
        save: Union[bool, None, str, Mapping],
        alias: str,
        pipeline_config: PipelineConfig,
    ) -> Mapping[str, Any]:

        assert isinstance(pipeline_config, PipelineConfig)

        if save in [False, None]:
            save_new: Dict[str, Any] = {}
        elif save is True:
            field_names = pipeline_config.structure.pipeline_outputs_schema.keys()
            save_new = create_save_config(field_names=field_names, aliases=alias)
        elif isinstance(save, str):
            field_names = pipeline_config.structure.pipeline_outputs_schema.keys()
            save_new = create_save_config(field_names=field_names, aliases=save)
        elif isinstance(save, Mapping):
            save_new = dict(save)
        else:
            raise ValueError(
                f"Invalid type '{type(save)}' for 'save' attribute: must be None, bool, string or Mapping."
            )

        return save_new

    def run(
        self,
        inputs: Optional[Mapping[str, Any]] = None,
        save: Union[None, bool, str, Mapping[str, Any]] = None,
    ) -> ValueMap:

        pipeline = Pipeline(
            structure=self.pipeline_config.structure,
            data_registry=self._kiara.data_registry,
        )
        pipeline_controller = SinglePipelineBatchController(
            pipeline=pipeline, job_registry=self._kiara.job_registry
        )

        run_inputs = dict(self.inputs)
        if inputs:
            run_inputs.update(inputs)

        pipeline.set_pipeline_inputs(inputs=run_inputs)
        pipeline_controller.process_pipeline()

        result = self._kiara.data_registry.load_values(
            pipeline.get_current_pipeline_outputs()
        )

        if save is not None:
            if save is True:
                save = self.save_defaults
            else:
                save = self.__class__.create_save_aliases(
                    save=save, alias=self.alias, pipeline_config=self.pipeline_config
                )

            self._kiara.save_values(values=result, alias_map=save)

        return result

Attributes

alias: str pydantic-field required

The batch name/alias.

inputs: Dict[str, Any] pydantic-field required

The (base) inputs to use. Can be augmented before running the operation.

pipeline_config: PipelineConfig pydantic-field required

The configuration of the underlying pipeline.

save_defaults: Dict[str, List[str]] pydantic-field

Configuration which values to save, under which alias(es).

Config

Source code in kiara/interfaces/python_api/batch.py

class Config:
    validate_assignment = True

add_alias(values) classmethod

Source code in kiara/interfaces/python_api/batch.py

@root_validator(pre=True)
def add_alias(cls, values):

    if not values.get("alias", None):
        pc = values.get("pipeline_config", None)
        if not pc:
            raise ValueError("No pipeline config provided.")
        if isinstance(pc, PipelineConfig):
            alias = pc.pipeline_name
        else:
            alias = pc.get("pipeline_name", None)
        values["alias"] = alias

    return values

create_save_aliases(save, alias, pipeline_config) classmethod

Source code in kiara/interfaces/python_api/batch.py

@classmethod
def create_save_aliases(
    cls,
    save: Union[bool, None, str, Mapping],
    alias: str,
    pipeline_config: PipelineConfig,
) -> Mapping[str, Any]:

    assert isinstance(pipeline_config, PipelineConfig)

    if save in [False, None]:
        save_new: Dict[str, Any] = {}
    elif save is True:
        field_names = pipeline_config.structure.pipeline_outputs_schema.keys()
        save_new = create_save_config(field_names=field_names, aliases=alias)
    elif isinstance(save, str):
        field_names = pipeline_config.structure.pipeline_outputs_schema.keys()
        save_new = create_save_config(field_names=field_names, aliases=save)
    elif isinstance(save, Mapping):
        save_new = dict(save)
    else:
        raise ValueError(
            f"Invalid type '{type(save)}' for 'save' attribute: must be None, bool, string or Mapping."
        )

    return save_new

from_config(data, kiara) classmethod

Source code in kiara/interfaces/python_api/batch.py

@classmethod
def from_config(
    cls,
    data: Mapping[str, Any],
    kiara: Optional["Kiara"],
):

    data = dict(data)
    inputs = data.pop("inputs", {})
    save = data.pop("save", False)
    pipeline_id = data.pop("pipeline_name", None)
    if pipeline_id is None:
        pipeline_id = str(uuid.uuid4())

    if kiara is None:
        kiara = Kiara.instance()

    pipeline_config = PipelineConfig.from_config(
        pipeline_name=pipeline_id, data=data, kiara=kiara
    )

    result = cls(pipeline_config=pipeline_config, inputs=inputs, save=save)
    result._kiara = kiara
    return result

from_file(path, kiara=None) classmethod

Source code in kiara/interfaces/python_api/batch.py

@classmethod
def from_file(
    cls,
    path: str,
    kiara: Optional["Kiara"] = None,
):

    data = get_data_from_file(path)
    pipeline_id = data.get("pipeline_name", None)
    if pipeline_id is None:
        name = os.path.basename(path)
        if name.endswith(".json"):
            name = name[0:-5]
        elif name.endswith(".yaml"):
            name = name[0:-5]
        data["pipeline_name"] = name

    return cls.from_config(data=data, kiara=kiara)

run(self, inputs=None, save=None)

Source code in kiara/interfaces/python_api/batch.py

def run(
    self,
    inputs: Optional[Mapping[str, Any]] = None,
    save: Union[None, bool, str, Mapping[str, Any]] = None,
) -> ValueMap:

    pipeline = Pipeline(
        structure=self.pipeline_config.structure,
        data_registry=self._kiara.data_registry,
    )
    pipeline_controller = SinglePipelineBatchController(
        pipeline=pipeline, job_registry=self._kiara.job_registry
    )

    run_inputs = dict(self.inputs)
    if inputs:
        run_inputs.update(inputs)

    pipeline.set_pipeline_inputs(inputs=run_inputs)
    pipeline_controller.process_pipeline()

    result = self._kiara.data_registry.load_values(
        pipeline.get_current_pipeline_outputs()
    )

    if save is not None:
        if save is True:
            save = self.save_defaults
        else:
            save = self.__class__.create_save_aliases(
                save=save, alias=self.alias, pipeline_config=self.pipeline_config
            )

        self._kiara.save_values(values=result, alias_map=save)

    return result

validate_save(save, values) classmethod

Source code in kiara/interfaces/python_api/batch.py

@validator("save_defaults", always=True, pre=True)
def validate_save(cls, save, values):

    alias = values["alias"]
    pipeline_config = values["pipeline_config"]
    return cls.create_save_aliases(
        save=save, alias=alias, pipeline_config=pipeline_config
    )

operation

KiaraOperation

Source code in kiara/interfaces/python_api/operation.py

class KiaraOperation(object):
    def __init__(
        self,
        kiara: "Kiara",
        operation_name: str,
        operation_config: Optional[Mapping[str, Any]] = None,
    ):

        self._kiara: Kiara = kiara
        self._operation_name: str = operation_name
        if operation_config is None:
            operation_config = {}
        else:
            operation_config = dict(operation_config)
        self._operation_config: Dict[str, Any] = operation_config

        self._inputs_raw: Dict[str, Any] = {}

        self._operation: Optional[Operation] = None
        self._inputs: Optional[ValueMap] = None

        self._job_config: Optional[JobConfig] = None

        self._queued_jobs: Dict[uuid.UUID, Dict[str, Any]] = {}
        self._last_job: Optional[uuid.UUID] = None
        self._results: Dict[uuid.UUID, ValueMap] = {}

        self._defaults: Optional[Dict[str, Any]] = None

    def validate(self):

        self.job_config  # noqa

    def _invalidate(self):

        self._job_config = None
        # self._defaults = None

    @property
    def operation_inputs(self) -> ValueMap:

        if self._inputs is not None:
            return self._inputs

        self._invalidate()
        if self._defaults is not None:
            data = dict(self._defaults)
        else:
            data = {}
        data.update(self._inputs_raw)

        self._inputs = self._kiara.data_registry.create_valueset(
            data, self.operation.inputs_schema
        )
        return self._inputs

    def set_input(self, field: Optional[str], value: Any = None):

        if field is None:
            if value is None:
                self._inputs_raw.clear()
                self._invalidate()
                return
            else:
                if not isinstance(value, Mapping):
                    raise Exception(
                        "Can't set inputs dictionary (if no key is provided, value must be 'None' or of type 'Mapping')."
                    )

                self._inputs_raw.clear()
                self.set_inputs(**value)
                self._invalidate()
                return
        else:
            old = self._inputs_raw.get(field, None)
            self._inputs_raw[field] = value
            if old != value:
                self._invalidate()
            return

    def set_inputs(self, **inputs: Any):

        changed = False
        for k, v in inputs.items():
            old = self._inputs_raw.get(k, None)
            self._inputs_raw[k] = v
            if old != v:
                changed = True

        if changed:
            self._invalidate()

        return

    def run(self, **inputs: Any) -> ValueMap:

        job_id = self.queue_job(**inputs)
        results = self.retrieve_result(job_id=job_id)
        return results

    @property
    def operation_name(self) -> str:
        return self._operation_name

    @operation_name.setter
    def operation_name(self, operation_name: str):
        self._operation_name = operation_name
        self._operation = None

    @property
    def operation_config(self) -> Mapping[str, Any]:
        return self._operation_config

    def set_operation_config_value(
        self, key: Optional[str], value: Any = None
    ) -> Mapping[str, Any]:

        if key is None:
            if value is None:
                old = bool(self._operation_config)
                self._operation_config.clear()
                if old:
                    self._operation = None
                return self._operation_config
            else:
                try:
                    old_conf = self._operation_config
                    self._operation_config = dict(value)
                    if old_conf != self._operation_config:
                        self._operation = None
                    return self._operation_config
                except Exception as e:
                    raise Exception(
                        f"Can't set configuration value dictionary (if no key is provided, value must be 'None' or of type 'Mapping'): {e}"
                    )

        self._operation_config[key] = value
        self._invalidate()
        return self._operation_config

    @property
    def operation(self) -> "Operation":

        if self._operation is not None:
            return self._operation

        self._invalidate()
        self._defaults = None

        module_or_operation = self._operation_name
        operation: Optional[Operation] = None

        if module_or_operation in self._kiara.operation_registry.operation_ids:

            operation = self._kiara.operation_registry.get_operation(
                module_or_operation
            )
            if self._operation_config:
                raise Exception(
                    f"Specified run target '{module_or_operation}' is an operation, additional module configuration is not allowed."
                )

        elif module_or_operation in self._kiara.module_type_names:

            manifest = Manifest(
                module_type=module_or_operation, module_config=self._operation_config
            )
            module = self._kiara.create_module(manifest=manifest)
            operation = Operation.create_from_module(module)

        elif os.path.isfile(module_or_operation):
            data = get_data_from_file(module_or_operation)
            pipeline_name = data.pop("pipeline_name", None)
            if pipeline_name is None:
                pipeline_name = os.path.basename(module_or_operation)

            self._defaults = data.pop("inputs", {})

            pipeline_config = PipelineConfig.from_config(
                pipeline_name=pipeline_name, data=data, kiara=self._kiara
            )

            manifest = self._kiara.create_manifest(
                "pipeline", config=pipeline_config.dict()
            )
            module = self._kiara.create_module(manifest=manifest)
            operation = Operation.create_from_module(module, doc=pipeline_config.doc)

        else:
            raise Exception(
                f"Can't assemble operation, invalid operation/module name: {module_or_operation}. Must be registered module or operation name, or file."
            )
            # manifest = Manifest(
            #     module_type=module_or_operation,
            #     module_config=self._operation_config,
            # )
            # module = self._kiara.create_module(manifest=manifest)
            # operation = Operation.create_from_module(module=module)

        if operation is None:

            merged = set(self._kiara.module_type_names)
            merged.update(self._kiara.operation_registry.operation_ids)
            raise NoSuchExecutionTargetException(
                selected_target=self.operation_name,
                msg=f"Invalid run target name '{module_or_operation}'. Must be a path to a pipeline file, or one of the available modules/operations.",
                available_targets=sorted(merged),
            )

        self._operation = operation
        return self._operation

    @property
    def job_config(self) -> JobConfig:

        if self._job_config is not None:
            return self._job_config

        self._job_config = self.operation.prepare_job_config(
            kiara=self._kiara, inputs=self.operation_inputs
        )
        return self._job_config

    def queue_job(self, **inputs) -> uuid.UUID:

        if inputs:
            self.set_inputs(**inputs)

        job_config = self.job_config
        operation = self.operation
        op_inputs = self.operation_inputs

        job_id = self._kiara.job_registry.execute_job(job_config=job_config, wait=False)

        self._queued_jobs[job_id] = {
            "job_config": job_config,
            "operation": operation,
            "inputs": op_inputs,
        }
        self._last_job = job_id
        return job_id

    def retrieve_result(self, job_id: Optional[uuid.UUID] = None) -> ValueMap:

        if job_id in self._results.keys():
            assert job_id is not None
            return self._results[job_id]

        if job_id is None:
            job_id = self._last_job

        if job_id is None:
            raise Exception("No job queued (yet).")

        operation: Operation = self._queued_jobs[job_id]["operation"]  # type: ignore

        status = self._kiara.job_registry.get_job_status(job_id=job_id)

        if status == JobStatus.FAILED:
            job = self._kiara.job_registry.get_active_job(job_id=job_id)
            raise FailedJobException(job=job)

        outputs = self._kiara.job_registry.retrieve_result(job_id)
        outputs = operation.process_job_outputs(outputs=outputs)
        self._results[job_id] = outputs
        return outputs

    def save_result(
        self,
        job_id: Optional[uuid.UUID] = None,
        aliases: Union[None, str, Mapping] = None,
    ) -> StoreValuesResult:

        if job_id is None:
            job_id = self._last_job

        if job_id is None:
            raise Exception("No job queued (yet).")

        result = self.retrieve_result(job_id=job_id)
        alias_map = create_save_config(field_names=result.field_names, aliases=aliases)

        store_result = self._kiara.save_values(values=result, alias_map=alias_map)

        self._kiara.job_registry.store_job_record(job_id=job_id)

        return store_result

    def create_renderable(self, **config: Any) -> RenderableType:

        show_operation_name = config.get("show_operation_name", True)
        show_operation_doc = config.get("show_operation_doc", True)
        show_inputs = config.get("show_inputs", False)
        show_outputs_schema = config.get("show_outputs_schema", False)

        items: List[Any] = []

        if show_operation_name:
            items.append(f"Operation: [bold]{self.operation_name}[/bold]")
        if show_operation_doc and self.operation.doc.is_set:
            items.append("")
            items.append(Markdown(self.operation.doc.full_doc, style="i"))

        if show_inputs:
            items.append("\nInputs:")
            try:
                op_inputs = self.operation_inputs
                inputs: Any = create_value_map_status_renderable(op_inputs)
            except InvalidValuesException as ive:
                inputs = ive.create_renderable(**config)
            except Exception as e:
                inputs = f"[red bold]{e}[/red bold]"
            items.append(inputs)
        if show_outputs_schema:
            items.append("\nOutputs:")
            outputs_schema = create_table_from_field_schemas(
                _add_default=False,
                _add_required=False,
                _show_header=True,
                _constants=None,
                **self.operation.outputs_schema,
            )
            items.append(outputs_schema)

        return Group(*items)

job_config: JobConfig property readonly

operation: Operation property readonly

operation_config: Mapping[str, Any] property readonly

operation_inputs: ValueMap property readonly

operation_name: str property writable

create_renderable(self, **config)

Source code in kiara/interfaces/python_api/operation.py

def create_renderable(self, **config: Any) -> RenderableType:

    show_operation_name = config.get("show_operation_name", True)
    show_operation_doc = config.get("show_operation_doc", True)
    show_inputs = config.get("show_inputs", False)
    show_outputs_schema = config.get("show_outputs_schema", False)

    items: List[Any] = []

    if show_operation_name:
        items.append(f"Operation: [bold]{self.operation_name}[/bold]")
    if show_operation_doc and self.operation.doc.is_set:
        items.append("")
        items.append(Markdown(self.operation.doc.full_doc, style="i"))

    if show_inputs:
        items.append("\nInputs:")
        try:
            op_inputs = self.operation_inputs
            inputs: Any = create_value_map_status_renderable(op_inputs)
        except InvalidValuesException as ive:
            inputs = ive.create_renderable(**config)
        except Exception as e:
            inputs = f"[red bold]{e}[/red bold]"
        items.append(inputs)
    if show_outputs_schema:
        items.append("\nOutputs:")
        outputs_schema = create_table_from_field_schemas(
            _add_default=False,
            _add_required=False,
            _show_header=True,
            _constants=None,
            **self.operation.outputs_schema,
        )
        items.append(outputs_schema)

    return Group(*items)

queue_job(self, **inputs)

Source code in kiara/interfaces/python_api/operation.py

def queue_job(self, **inputs) -> uuid.UUID:

    if inputs:
        self.set_inputs(**inputs)

    job_config = self.job_config
    operation = self.operation
    op_inputs = self.operation_inputs

    job_id = self._kiara.job_registry.execute_job(job_config=job_config, wait=False)

    self._queued_jobs[job_id] = {
        "job_config": job_config,
        "operation": operation,
        "inputs": op_inputs,
    }
    self._last_job = job_id
    return job_id

retrieve_result(self, job_id=None)

Source code in kiara/interfaces/python_api/operation.py

def retrieve_result(self, job_id: Optional[uuid.UUID] = None) -> ValueMap:

    if job_id in self._results.keys():
        assert job_id is not None
        return self._results[job_id]

    if job_id is None:
        job_id = self._last_job

    if job_id is None:
        raise Exception("No job queued (yet).")

    operation: Operation = self._queued_jobs[job_id]["operation"]  # type: ignore

    status = self._kiara.job_registry.get_job_status(job_id=job_id)

    if status == JobStatus.FAILED:
        job = self._kiara.job_registry.get_active_job(job_id=job_id)
        raise FailedJobException(job=job)

    outputs = self._kiara.job_registry.retrieve_result(job_id)
    outputs = operation.process_job_outputs(outputs=outputs)
    self._results[job_id] = outputs
    return outputs

run(self, **inputs)

Source code in kiara/interfaces/python_api/operation.py

def run(self, **inputs: Any) -> ValueMap:

    job_id = self.queue_job(**inputs)
    results = self.retrieve_result(job_id=job_id)
    return results

save_result(self, job_id=None, aliases=None)

Source code in kiara/interfaces/python_api/operation.py

def save_result(
    self,
    job_id: Optional[uuid.UUID] = None,
    aliases: Union[None, str, Mapping] = None,
) -> StoreValuesResult:

    if job_id is None:
        job_id = self._last_job

    if job_id is None:
        raise Exception("No job queued (yet).")

    result = self.retrieve_result(job_id=job_id)
    alias_map = create_save_config(field_names=result.field_names, aliases=aliases)

    store_result = self._kiara.save_values(values=result, alias_map=alias_map)

    self._kiara.job_registry.store_job_record(job_id=job_id)

    return store_result

set_input(self, field, value=None)

Source code in kiara/interfaces/python_api/operation.py

def set_input(self, field: Optional[str], value: Any = None):

    if field is None:
        if value is None:
            self._inputs_raw.clear()
            self._invalidate()
            return
        else:
            if not isinstance(value, Mapping):
                raise Exception(
                    "Can't set inputs dictionary (if no key is provided, value must be 'None' or of type 'Mapping')."
                )

            self._inputs_raw.clear()
            self.set_inputs(**value)
            self._invalidate()
            return
    else:
        old = self._inputs_raw.get(field, None)
        self._inputs_raw[field] = value
        if old != value:
            self._invalidate()
        return

set_inputs(self, **inputs)

Source code in kiara/interfaces/python_api/operation.py

def set_inputs(self, **inputs: Any):

    changed = False
    for k, v in inputs.items():
        old = self._inputs_raw.get(k, None)
        self._inputs_raw[k] = v
        if old != v:
            changed = True

    if changed:
        self._invalidate()

    return

set_operation_config_value(self, key, value=None)

Source code in kiara/interfaces/python_api/operation.py

def set_operation_config_value(
    self, key: Optional[str], value: Any = None
) -> Mapping[str, Any]:

    if key is None:
        if value is None:
            old = bool(self._operation_config)
            self._operation_config.clear()
            if old:
                self._operation = None
            return self._operation_config
        else:
            try:
                old_conf = self._operation_config
                self._operation_config = dict(value)
                if old_conf != self._operation_config:
                    self._operation = None
                return self._operation_config
            except Exception as e:
                raise Exception(
                    f"Can't set configuration value dictionary (if no key is provided, value must be 'None' or of type 'Mapping'): {e}"
                )

    self._operation_config[key] = value
    self._invalidate()
    return self._operation_config

validate(self)

Source code in kiara/interfaces/python_api/operation.py

def validate(self):

    self.job_config  # noqa

utils

create_save_config(field_names, aliases)

Source code in kiara/interfaces/python_api/utils.py

def create_save_config(
    field_names: Union[str, Iterable[str]],
    aliases: Union[None, str, Iterable[str], Mapping[str, Any]],
) -> Dict[str, List[str]]:

    if isinstance(field_names, str):
        field_names = [field_names]

    if aliases is None:
        alias_map: Dict[str, List[str]] = {}
    elif isinstance(aliases, str):
        alias_map = {}
        for field_name in field_names:
            alias_map[field_name] = [f"{aliases}.{field_name}"]
    elif isinstance(aliases, Mapping):
        alias_map = {}
        for field_name in aliases.keys():
            if field_name in field_names:
                if isinstance(aliases[field_name], str):
                    alias_map[field_name] = [aliases[field_name]]
                else:
                    alias_map[field_name] = sorted(aliases[field_name])
            else:
                logger.warning(
                    "ignore.field_alias",
                    ignored_field_name=field_name,
                    reason="field name not in results",
                    available_field_names=sorted(field_names),
                )
                continue
    else:
        raise Exception(
            f"Invalid type '{type(aliases)}' for aliases parameter, must be string or mapping."
        )

    return alias_map

tui special

Modules

pager

Classes

PagerApp (App)

Source code in kiara/interfaces/tui/pager.py

class PagerApp(App):
    def __init__(self, **kwargs):

        self._control = PagerControl()
        self._pager = ValuePager()

        self._pager._render_op = kwargs.pop("operation")
        self._pager._value = kwargs.pop("value")
        self._pager._kiara = kwargs.pop("kiara")
        self._pager._control_widget = self._control
        self._control._pager = self._pager

        super().__init__(**kwargs)

    # async def on_mount(self) -> None:
    #
    #     await self.view.dock(Footer(), edge="bottom")
    #     await self.view.dock(self._pager, name="data")

    async def on_mount(self) -> None:

        await self.view.dock(self._pager, self._control, edge="top")
        await self.view.dock(self._control, edge="bottom", size=10)

    async def on_load(self, event):

        await self.bind("q", "quit", "Quit")

    async def on_key(self, event):

        self._control.key_pressed(event.key)

on_key(self, event) async

Source code in kiara/interfaces/tui/pager.py

async def on_key(self, event):

    self._control.key_pressed(event.key)

on_load(self, event) async

Source code in kiara/interfaces/tui/pager.py

async def on_load(self, event):

    await self.bind("q", "quit", "Quit")

on_mount(self) async

Source code in kiara/interfaces/tui/pager.py

async def on_mount(self) -> None:

    await self.view.dock(self._pager, self._control, edge="top")
    await self.view.dock(self._control, edge="bottom", size=10)

PagerControl (Widget)

Source code in kiara/interfaces/tui/pager.py

class PagerControl(Widget):

    _pager: ValuePager = None  # type: ignore
    _instruction_keys: Dict[str, str] = None  # type: ignore

    render_metadata: RenderMetadata = Reactive(None)  # type: ignore

    def key_pressed(self, key: str):

        if key in self._instruction_keys.keys():
            command = self._instruction_keys[key]
            new_ri = self.render_metadata.related_instructions[command].dict()
            self._pager.update_render_instruction(new_ri)

    def render(self) -> RenderableType:

        instruction_keys: Dict[str, str] = {}
        output = []
        for key in self.render_metadata.related_instructions.keys():
            instruction_keys[key[0:1]] = key
            output.append(f"\[{key[0:1]}]{key[1:]}")  # noqa: W605

        output.append("\[q]uit")  # noqa: W605

        self._instruction_keys = instruction_keys

        return "\n".join(output)

can_focus

render_metadata

Methods

key_pressed(self, key)

Source code in kiara/interfaces/tui/pager.py

def key_pressed(self, key: str):

    if key in self._instruction_keys.keys():
        command = self._instruction_keys[key]
        new_ri = self.render_metadata.related_instructions[command].dict()
        self._pager.update_render_instruction(new_ri)

render(self)

Get renderable for widget.

Returns:

Type	Description
RenderableType	Any renderable

Source code in kiara/interfaces/tui/pager.py

def render(self) -> RenderableType:

    instruction_keys: Dict[str, str] = {}
    output = []
    for key in self.render_metadata.related_instructions.keys():
        instruction_keys[key[0:1]] = key
        output.append(f"\[{key[0:1]}]{key[1:]}")  # noqa: W605

    output.append("\[q]uit")  # noqa: W605

    self._instruction_keys = instruction_keys

    return "\n".join(output)

ValuePager (Widget)

Source code in kiara/interfaces/tui/pager.py

class ValuePager(Widget):

    _render_op: Operation = None  # type: ignore
    _kiara: Kiara = None  # type: ignore
    _value: Value = None  # type: ignore
    _control_widget = None  # type: ignore

    render_instruction: Optional[Mapping[str, Any]] = Reactive(None)  # type: ignore

    current_rendered_value: Optional[RenderableType] = None  # type: ignore
    current_render_metadata: Optional[RenderMetadata] = None  # type: ignore

    def update_render_instruction(self, render_metadata: Mapping[str, Any]):

        rows = self.size.height - 4
        if rows <= 0:
            rows = 1

        new_ri = dict(render_metadata)
        new_ri["number_of_rows"] = rows

        self.render_instruction = new_ri

    def render(self) -> RenderableType:

        if self.render_instruction is None:
            self.update_render_instruction({})

        result = self._render_op.run(
            kiara=self._kiara,
            inputs={
                "value": self._value,
                "render_instruction": self.render_instruction,
            },
        )

        self.current_rendered_value = result.get_value_data("rendered_value")  # type: ignore
        self.current_render_metadata = result.get_value_data("render_metadata")  # type: ignore

        self._control_widget.render_metadata = self.current_render_metadata  # type: ignore

        return self.current_rendered_value  # type: ignore

can_focus

current_render_metadata

current_rendered_value

render_instruction

Methods

render(self)

Get renderable for widget.

Returns:

Type	Description
RenderableType	Any renderable

Source code in kiara/interfaces/tui/pager.py

def render(self) -> RenderableType:

    if self.render_instruction is None:
        self.update_render_instruction({})

    result = self._render_op.run(
        kiara=self._kiara,
        inputs={
            "value": self._value,
            "render_instruction": self.render_instruction,
        },
    )

    self.current_rendered_value = result.get_value_data("rendered_value")  # type: ignore
    self.current_render_metadata = result.get_value_data("render_metadata")  # type: ignore

    self._control_widget.render_metadata = self.current_render_metadata  # type: ignore

    return self.current_rendered_value  # type: ignore

update_render_instruction(self, render_metadata)

Source code in kiara/interfaces/tui/pager.py

def update_render_instruction(self, render_metadata: Mapping[str, Any]):

    rows = self.size.height - 4
    if rows <= 0:
        rows = 1

    new_ri = dict(render_metadata)
    new_ri["number_of_rows"] = rows

    self.render_instruction = new_ri

models special

Classes

KiaraModel (ABC, BaseModel, JupyterMixin) pydantic-model

Base class that all models in kiara inherit from.

This class provides utility functions for things like rendering the model on terminal or as html, integration into a tree hierarchy of the overall kiara context, hashing, etc.

Source code in kiara/models/__init__.py

class KiaraModel(ABC, BaseModel, JupyterMixin):
    """Base class that all models in kiara inherit from.

    This class provides utility functions for things like rendering the model on terminal or as html, integration into
    a tree hierarchy of the overall kiara context, hashing, etc.
    """

    __slots__ = ["__weakref__"]

    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps
        extra = Extra.forbid

        # allow_mutation = False

    @classmethod
    def get_schema_hash(cls) -> int:
        if cls._schema_hash_cache is not None:
            return cls._schema_hash_cache

        obj = cls.schema_json()
        h = DeepHash(obj, hasher=KIARA_HASH_FUNCTION)
        cls._schema_hash_cache = h[obj]
        return cls._schema_hash_cache

    _graph_cache: Optional[nx.DiGraph] = PrivateAttr(default=None)
    _subcomponent_names_cache: Optional[List[str]] = PrivateAttr(default=None)
    _dynamic_subcomponents: Dict[str, "KiaraModel"] = PrivateAttr(default_factory=dict)
    _id_cache: Optional[str] = PrivateAttr(default=None)
    _category_id_cache: Optional[str] = PrivateAttr(default=None)
    _schema_hash_cache: ClassVar = None
    _cid_cache: Optional[CID] = PrivateAttr(default=None)
    _dag_cache: Optional[bytes] = PrivateAttr(default=None)
    _size_cache: Optional[int] = PrivateAttr(default=None)

    def _retrieve_data_to_hash(self) -> EncodableType:
        """Return data important for hashing this model instance. Implemented by sub-classes.

        This returns the relevant data that makes this model unique, excluding any secondary metadata that is not
        necessary for this model to be used functionally. Like for example documentation.
        """

        return self.dict()

    @property
    def instance_id(self) -> str:
        """The unique id of this model, within its category."""

        if self._id_cache is not None:
            return self._id_cache

        self._id_cache = self._retrieve_id()
        return self._id_cache

    @property
    def instance_cid(self) -> CID:
        if self._cid_cache is None:
            self._compute_cid()
        return self._cid_cache  # type: ignore

    @property
    def instance_dag(self) -> bytes:

        if self._dag_cache is None:
            self._compute_cid()
        return self._dag_cache  # type: ignore

    @property
    def instance_size(self) -> int:

        if self._size_cache is None:
            self._compute_cid()
        return self._size_cache  # type: ignore

    @property
    def model_type_id(self) -> str:
        """The id of the category of this model."""

        if hasattr(self.__class__, "_kiara_model_id"):
            return self._kiara_model_id  # type: ignore
        else:
            return _default_id_func(self.__class__)

    def _retrieve_id(self) -> str:
        return str(self.instance_cid)

    def _compute_cid(self):
        """A hash for this model."""
        if self._cid_cache is not None:
            return

        obj = self._retrieve_data_to_hash()
        dag, cid = compute_cid(data=obj)

        self._cid_cache = cid
        self._dag_cache = dag
        self._size_cache = len(dag)

    # ==========================================================================================
    # subcomponent related methods
    @property
    def subcomponent_keys(self) -> Iterable[str]:
        """The keys of available sub-components of this model."""

        if self._subcomponent_names_cache is None:
            self._subcomponent_names_cache = sorted(self._retrieve_subcomponent_keys())
        return self._subcomponent_names_cache

    @property
    def subcomponent_tree(self) -> Optional[nx.DiGraph]:
        """A tree structure, containing all sub-components (and their subcomponents) of this model."""
        if not self.subcomponent_keys:
            return None

        if self._graph_cache is None:
            self._graph_cache = assemble_subcomponent_tree(self)
        return self._graph_cache

    def get_subcomponent(self, path: str) -> "KiaraModel":
        """Retrieve the subcomponent identified by the specified path."""

        if path not in self._dynamic_subcomponents.keys():
            self._dynamic_subcomponents[path] = self._retrieve_subcomponent(path=path)
        return self._dynamic_subcomponents[path]

    def find_subcomponents(self, category: str) -> Dict[str, "KiaraModel"]:
        """Find and return all subcomponents of this model that are member of the specified category."""
        tree = self.subcomponent_tree
        if tree is None:
            raise Exception(f"No subcomponents found for category: {category}")

        result = {}
        for node_id, node in tree.nodes.items():
            if not hasattr(node["obj"], "get_category_alias"):
                raise NotImplementedError()

            if category != node["obj"].get_category_alias():
                continue

            n_id = node_id[9:]  # remove the __self__. token
            result[n_id] = node["obj"]
        return result

    def _retrieve_subcomponent_keys(self) -> Iterable[str]:
        """Retrieve the keys of all subcomponents of this model.

        Can be overwritten in sub-classes, by default it tries to automatically determine the subcomponents.
        """

        return retrieve_data_subcomponent_keys(self)

    def _retrieve_subcomponent(self, path: str) -> "KiaraModel":
        """Retrieve the subcomponent under the specified path.

        Can be overwritten in sub-classes, by default it tries to automatically determine the subcomponents.
        """

        m = get_subcomponent_from_model(self, path=path)
        return m

    # ==========================================================================================
    # model rendering related methods
    def create_panel(self, title: str = None, **config: Any) -> Panel:

        rend = self.create_renderable(**config)
        return Panel(rend, box=box.ROUNDED, title=title, title_align="left")

    def create_renderable(self, **config: Any) -> RenderableType:

        from kiara.utils.output import extract_renderable

        include = config.get("include", None)

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("Key", style="i")
        table.add_column("Value")
        for k in self.__fields__.keys():
            if include is not None and k not in include:
                continue
            attr = getattr(self, k)
            v = extract_renderable(attr)
            table.add_row(k, v)
        return table

    def create_html(self, **config) -> str:

        r = self.create_renderable()
        if hasattr(r, "_repr_mimebundle_"):
            mime_bundle = r._repr_mimebundle_(include=[], exclude=[])  # type: ignore
        else:
            raise NotImplementedError(
                f"Type '{self.__class__}' can't be rendered as html (yet)."
            )

        return mime_bundle["text/html"]

    def as_dict_with_schema(self) -> Dict[str, Dict[str, Any]]:
        return {"data": self.dict(), "schema": self.schema()}

    def as_json_with_schema(self) -> str:

        data_json = self.json()
        schema_json = self.schema_json()
        return '{"data": ' + data_json + ', "schema": ' + schema_json + "}"

    def __hash__(self):
        return int.from_bytes(self.instance_cid.digest, "big")

    def __eq__(self, other):

        if self.__class__ != other.__class__:
            return False
        else:
            return (self.instance_id, self.instance_cid) == (
                other.instance_id,
                other.instance_cid,
            )

    def __repr__(self):

        try:
            model_id = self.instance_id
        except Exception:
            model_id = "-- n/a --"

        return f"{self.__class__.__name__}(model_id={model_id}, category={self.model_type_id}, fields=[{', '.join(self.__fields__.keys())}])"

    def __str__(self):
        return self.__repr__()

    def __rich_console__(
        self, console: Console, options: ConsoleOptions
    ) -> RenderResult:

        yield self.create_renderable()

Attributes

instance_cid: CID property readonly

instance_dag: bytes property readonly

instance_id: str property readonly

The unique id of this model, within its category.

instance_size: int property readonly

model_type_id: str property readonly

The id of the category of this model.

subcomponent_keys: Iterable[str] property readonly

The keys of available sub-components of this model.

subcomponent_tree: Optional[networkx.classes.digraph.DiGraph] property readonly

A tree structure, containing all sub-components (and their subcomponents) of this model.

Config

Source code in kiara/models/__init__.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps
    extra = Extra.forbid

    # allow_mutation = False

extra

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/models/__init__.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

Methods

as_dict_with_schema(self)

Source code in kiara/models/__init__.py

def as_dict_with_schema(self) -> Dict[str, Dict[str, Any]]:
    return {"data": self.dict(), "schema": self.schema()}

as_json_with_schema(self)

Source code in kiara/models/__init__.py

def as_json_with_schema(self) -> str:

    data_json = self.json()
    schema_json = self.schema_json()
    return '{"data": ' + data_json + ', "schema": ' + schema_json + "}"

create_html(self, **config)

Source code in kiara/models/__init__.py

def create_html(self, **config) -> str:

    r = self.create_renderable()
    if hasattr(r, "_repr_mimebundle_"):
        mime_bundle = r._repr_mimebundle_(include=[], exclude=[])  # type: ignore
    else:
        raise NotImplementedError(
            f"Type '{self.__class__}' can't be rendered as html (yet)."
        )

    return mime_bundle["text/html"]

create_panel(self, title=None, **config)

Source code in kiara/models/__init__.py

def create_panel(self, title: str = None, **config: Any) -> Panel:

    rend = self.create_renderable(**config)
    return Panel(rend, box=box.ROUNDED, title=title, title_align="left")

create_renderable(self, **config)

Source code in kiara/models/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:

    from kiara.utils.output import extract_renderable

    include = config.get("include", None)

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("Key", style="i")
    table.add_column("Value")
    for k in self.__fields__.keys():
        if include is not None and k not in include:
            continue
        attr = getattr(self, k)
        v = extract_renderable(attr)
        table.add_row(k, v)
    return table

find_subcomponents(self, category)

Find and return all subcomponents of this model that are member of the specified category.

Source code in kiara/models/__init__.py

def find_subcomponents(self, category: str) -> Dict[str, "KiaraModel"]:
    """Find and return all subcomponents of this model that are member of the specified category."""
    tree = self.subcomponent_tree
    if tree is None:
        raise Exception(f"No subcomponents found for category: {category}")

    result = {}
    for node_id, node in tree.nodes.items():
        if not hasattr(node["obj"], "get_category_alias"):
            raise NotImplementedError()

        if category != node["obj"].get_category_alias():
            continue

        n_id = node_id[9:]  # remove the __self__. token
        result[n_id] = node["obj"]
    return result

get_schema_hash() classmethod

Source code in kiara/models/__init__.py

@classmethod
def get_schema_hash(cls) -> int:
    if cls._schema_hash_cache is not None:
        return cls._schema_hash_cache

    obj = cls.schema_json()
    h = DeepHash(obj, hasher=KIARA_HASH_FUNCTION)
    cls._schema_hash_cache = h[obj]
    return cls._schema_hash_cache

get_subcomponent(self, path)

Retrieve the subcomponent identified by the specified path.

Source code in kiara/models/__init__.py

def get_subcomponent(self, path: str) -> "KiaraModel":
    """Retrieve the subcomponent identified by the specified path."""

    if path not in self._dynamic_subcomponents.keys():
        self._dynamic_subcomponents[path] = self._retrieve_subcomponent(path=path)
    return self._dynamic_subcomponents[path]

Modules

aliases special

VALUE_ALIAS_SEPARATOR

logger

Classes

AliasValueMap (ValueMap) pydantic-model

Source code in kiara/models/aliases/__init__.py

class AliasValueMap(ValueMap):

    _kiara_model_id = "instance.value_map.aliases"

    alias: Optional[str] = Field(description="This maps own (full) alias.")
    version: int = Field(description="The version of this map (in this maps parent).")
    created: Optional[datetime.datetime] = Field(
        description="The time this map was created."
    )
    assoc_schema: Optional[ValueSchema] = Field(
        description="The schema for this maps associated value."
    )
    assoc_value: Optional[uuid.UUID] = Field(
        description="The value that is associated with this map."
    )

    value_items: Dict[str, Dict[int, "AliasValueMap"]] = Field(
        description="The values contained in this set.", default_factory=dict
    )

    _data_registry: "DataRegistry" = PrivateAttr(default=None)
    _schema_locked: bool = PrivateAttr(default=False)
    _auto_schema: bool = PrivateAttr(default=True)
    _is_stored: bool = PrivateAttr(default=False)

    def _retrieve_data_to_hash(self) -> Any:
        raise NotImplementedError()

    @property
    def is_stored(self) -> bool:
        return self._is_stored

    def get_child_map(
        self, field_name: str, version: Optional[str] = None
    ) -> Optional["AliasValueMap"]:
        """Get the child map for the specified field / version combination.

        Raises an error if the child field does not exist. Returns 'None' if not value is set yet (but schema is).
        """

        if version is not None:
            raise NotImplementedError()

        if VALUE_ALIAS_SEPARATOR not in field_name:

            if self.values_schema.get(field_name, None) is None:
                raise KeyError(
                    f"No field name '{field_name}'. Available fields: {', '.join(self.values_schema.keys())}"
                )

            field_items = self.value_items[field_name]
            if not field_items:
                return None

            max_version = max(field_items.keys())

            item = field_items[max_version]
            return item

        else:
            child, rest = field_name.split(VALUE_ALIAS_SEPARATOR, maxsplit=1)
            if child not in self.values_schema.keys():
                raise Exception(
                    f"No field name '{child}'. Available fields: {', '.join(self.values_schema.keys())}"
                )
            child_map = self.get_child_map(child)
            assert child_map is not None
            return child_map.get_child_map(rest)

    def get_value_obj(self, field_name: str) -> Value:

        item = self.get_child_map(field_name=field_name)
        if item is None:
            return self._data_registry.NONE_VALUE
        if item.assoc_value is None:
            raise Exception(f"No value associated for field '{field_name}'.")

        return self._data_registry.get_value(value_id=item.assoc_value)

    def get_value_id(self, field_name: str) -> uuid.UUID:

        item = self.get_child_map(field_name=field_name)
        if item is None:
            return NONE_VALUE_ID
        else:
            return item.assoc_value if item.assoc_value is not None else NONE_VALUE_ID

    def get_all_value_ids(
        self,
    ) -> Dict[str, uuid.UUID]:

        result: Dict[str, uuid.UUID] = {}
        for k in self.values_schema.keys():
            v_id = self.get_value_id(field_name=k)
            if v_id is None:
                v_id = NONE_VALUE_ID
            result[k] = v_id
        return result

    def set_value(self, field_name: str, data: Any) -> None:

        assert VALUE_ALIAS_SEPARATOR not in field_name

        value = self._data_registry.register_data(data)
        self.set_alias(alias=field_name, value_id=value.value_id)

    def set_alias_schema(self, alias: str, schema: ValueSchema):

        if self._schema_locked:
            raise Exception(f"Can't add schema for alias '{alias}': schema locked.")

        if VALUE_ALIAS_SEPARATOR not in alias:

            self._set_local_field_schema(field_name=alias, schema=schema)
        else:
            child, rest = alias.split(VALUE_ALIAS_SEPARATOR, maxsplit=1)

            if child in self.values_schema.keys():
                child_map = self.get_child_map(child)
            else:
                self._set_local_field_schema(
                    field_name=child, schema=ValueSchema(type="none")
                )
                child_map = self.set_alias(alias=child, value_id=None)

            assert child_map is not None

            child_map.set_alias_schema(alias=rest, schema=schema)

    def _set_local_field_schema(self, field_name: str, schema: ValueSchema):

        assert field_name is not None
        if VALUE_ALIAS_SEPARATOR in field_name:
            raise Exception(
                f"Can't add schema, field name has alias separator in name: {field_name}. This is most likely a bug."
            )

        if field_name in self.values_schema.keys():
            raise Exception(
                f"Can't set alias schema for '{field_name}' to map: schema already set."
            )

        try:
            items = self.get_child_map(field_name)
            if items is not None:
                raise Exception(
                    f"Can't set schema for field '{field_name}': already at least one child set for this field."
                )
        except KeyError:
            pass

        self.values_schema[field_name] = schema
        self.value_items[field_name] = {}

    def get_alias(self, alias: str) -> Optional["AliasValueMap"]:

        if VALUE_ALIAS_SEPARATOR not in alias:
            if "@" in alias:
                raise NotImplementedError()

            child_map = self.get_child_map(alias)
            if child_map is None:
                return None

            return child_map

        else:
            child, rest = alias.split(VALUE_ALIAS_SEPARATOR, maxsplit=1)
            if "@" in child:
                raise NotImplementedError()

            child_map = self.get_child_map(field_name=child)

            if child_map is None:
                return None

            return child_map.get_alias(rest)

    def set_aliases(self, **aliases) -> Mapping[str, "AliasValueMap"]:

        result = {}
        for k, v in aliases.items():
            r = self.set_alias(alias=k, value_id=v)
            result[k] = r

        return result

    def set_alias(self, alias: str, value_id: Optional[uuid.UUID]) -> "AliasValueMap":

        if VALUE_ALIAS_SEPARATOR not in alias:
            child = None
            field_name: Optional[str] = alias
            rest = None
        else:
            child, rest = alias.split(VALUE_ALIAS_SEPARATOR, maxsplit=1)
            field_name = None

        if child is None:
            # means we are setting the alias in this map
            assert field_name is not None
            new_map = self._set_local_value_item(
                field_name=field_name, value_id=value_id
            )
            return new_map
        else:
            # means we are dealing with an intermediate alias map
            assert rest is not None
            assert child is not None
            assert field_name is None
            if child not in self.value_items.keys():
                if not self._auto_schema:
                    raise Exception(
                        f"Can't set alias '{alias}', no schema set for field: '{child}'."
                    )
                else:
                    self.set_alias_schema(alias=child, schema=ValueSchema(type="any"))

            field_item: Optional[AliasValueMap] = None
            try:
                field_item = self.get_child_map(field_name=child)
            except KeyError:
                pass

            if self.alias:
                new_alias = f"{self.alias}.{child}"
            else:
                new_alias = child

            if field_item is None:
                new_version = 0
                schemas = {}
                self.value_items[child] = {}
            else:
                max_version = len(field_item.keys())
                new_version = max_version + 1
                assert field_item.alias == new_alias
                assert field_item.version == max_version
                schemas = field_item.values_schema

            new_map = AliasValueMap(
                alias=new_alias,
                version=new_version,
                assoc_schema=self.values_schema[child],
                assoc_value=None,
                values_schema=schemas,
            )
            new_map._data_registry = self._data_registry
            self.value_items[child][new_version] = new_map

            new_map.set_alias(alias=rest, value_id=value_id)

        return new_map

    def _set_local_value_item(
        self, field_name: str, value_id: Optional[uuid.UUID] = None
    ) -> "AliasValueMap":

        assert VALUE_ALIAS_SEPARATOR not in field_name

        value: Optional[Value] = None
        if value_id is not None:
            value = self._data_registry.get_value(value_id=value_id)
            assert value is not None
            assert value.value_id == value_id

        if field_name not in self.values_schema.keys():
            if not self._auto_schema:
                raise Exception(
                    f"Can't add value for field '{field_name}': field not in schema."
                )
            else:
                if value_id is None:
                    value_schema = ValueSchema(type="none")
                else:
                    value_schema = value.value_schema  # type: ignore
                self.set_alias_schema(alias=field_name, schema=value_schema)

        field_items = self.value_items.get(field_name, None)
        if not field_items:
            assert field_items is not None
            new_version = 0
            values_schema = {}
        else:
            max_version = max(field_items.keys())
            current_map = field_items[max_version]

            if value_id == current_map.assoc_value:
                logger.debug(
                    "set_field.skip",
                    value_id=None,
                    reason=f"Same value id: {value_id}",
                )
                return current_map

            # TODO: check schema
            new_version = max(field_items.keys()) + 1
            values_schema = current_map.values_schema

        if self.alias:
            new_alias = f"{self.alias}.{field_name}"
        else:
            new_alias = field_name
        new_map = AliasValueMap(
            alias=new_alias,
            version=new_version,
            assoc_schema=self.values_schema[field_name],
            assoc_value=value_id,
            values_schema=values_schema,
        )
        new_map._data_registry = self._data_registry
        self.value_items[field_name][new_version] = new_map
        return new_map

    def print_tree(self):

        t = self.get_tree("base")
        terminal_print(t)

    def get_tree(self, base_name: str) -> Tree:

        if self.assoc_schema:
            type_name = self.assoc_schema.type
        else:
            type_name = "none"

        if type_name == "none":
            type_str = ""
        else:
            type_str = f" ({type_name})"

        tree = Tree(f"{base_name}{type_str}")
        if self.assoc_value:
            data = tree.add("__data__")
            value = self._data_registry.get_value(self.assoc_value)
            data.add(str(value.data))

        for field_name, schema in self.values_schema.items():

            alias = self.get_alias(alias=field_name)
            if alias is not None:
                tree.add(alias.get_tree(base_name=field_name))
            else:
                if schema.type == "none":
                    type_str = ""
                else:
                    type_str = f" ({schema.type})"

                tree.add(f"{field_name}{type_str}")

        return tree

    def __repr__(self):

        return f"AliasMap(assoc_value={self.assoc_value}, field_names={self.value_items.keys()})"

    def __str__(self):
        return self.__repr__()

Attributes

alias: str pydantic-field

This maps own (full) alias.

assoc_schema: ValueSchema pydantic-field

The schema for this maps associated value.

assoc_value: UUID pydantic-field

The value that is associated with this map.

created: datetime pydantic-field

The time this map was created.

is_stored: bool property readonly

value_items: Dict[str, Dict[int, AliasValueMap]] pydantic-field

The values contained in this set.

version: int pydantic-field required

The version of this map (in this maps parent).

Methods

get_alias(self, alias)

Source code in kiara/models/aliases/__init__.py

def get_alias(self, alias: str) -> Optional["AliasValueMap"]:

    if VALUE_ALIAS_SEPARATOR not in alias:
        if "@" in alias:
            raise NotImplementedError()

        child_map = self.get_child_map(alias)
        if child_map is None:
            return None

        return child_map

    else:
        child, rest = alias.split(VALUE_ALIAS_SEPARATOR, maxsplit=1)
        if "@" in child:
            raise NotImplementedError()

        child_map = self.get_child_map(field_name=child)

        if child_map is None:
            return None

        return child_map.get_alias(rest)

get_all_value_ids(self)

Source code in kiara/models/aliases/__init__.py

def get_all_value_ids(
    self,
) -> Dict[str, uuid.UUID]:

    result: Dict[str, uuid.UUID] = {}
    for k in self.values_schema.keys():
        v_id = self.get_value_id(field_name=k)
        if v_id is None:
            v_id = NONE_VALUE_ID
        result[k] = v_id
    return result

get_child_map(self, field_name, version=None)

Get the child map for the specified field / version combination.

Raises an error if the child field does not exist. Returns 'None' if not value is set yet (but schema is).

Source code in kiara/models/aliases/__init__.py

def get_child_map(
    self, field_name: str, version: Optional[str] = None
) -> Optional["AliasValueMap"]:
    """Get the child map for the specified field / version combination.

    Raises an error if the child field does not exist. Returns 'None' if not value is set yet (but schema is).
    """

    if version is not None:
        raise NotImplementedError()

    if VALUE_ALIAS_SEPARATOR not in field_name:

        if self.values_schema.get(field_name, None) is None:
            raise KeyError(
                f"No field name '{field_name}'. Available fields: {', '.join(self.values_schema.keys())}"
            )

        field_items = self.value_items[field_name]
        if not field_items:
            return None

        max_version = max(field_items.keys())

        item = field_items[max_version]
        return item

    else:
        child, rest = field_name.split(VALUE_ALIAS_SEPARATOR, maxsplit=1)
        if child not in self.values_schema.keys():
            raise Exception(
                f"No field name '{child}'. Available fields: {', '.join(self.values_schema.keys())}"
            )
        child_map = self.get_child_map(child)
        assert child_map is not None
        return child_map.get_child_map(rest)

get_tree(self, base_name)

Source code in kiara/models/aliases/__init__.py

def get_tree(self, base_name: str) -> Tree:

    if self.assoc_schema:
        type_name = self.assoc_schema.type
    else:
        type_name = "none"

    if type_name == "none":
        type_str = ""
    else:
        type_str = f" ({type_name})"

    tree = Tree(f"{base_name}{type_str}")
    if self.assoc_value:
        data = tree.add("__data__")
        value = self._data_registry.get_value(self.assoc_value)
        data.add(str(value.data))

    for field_name, schema in self.values_schema.items():

        alias = self.get_alias(alias=field_name)
        if alias is not None:
            tree.add(alias.get_tree(base_name=field_name))
        else:
            if schema.type == "none":
                type_str = ""
            else:
                type_str = f" ({schema.type})"

            tree.add(f"{field_name}{type_str}")

    return tree

get_value_id(self, field_name)

Source code in kiara/models/aliases/__init__.py

def get_value_id(self, field_name: str) -> uuid.UUID:

    item = self.get_child_map(field_name=field_name)
    if item is None:
        return NONE_VALUE_ID
    else:
        return item.assoc_value if item.assoc_value is not None else NONE_VALUE_ID

get_value_obj(self, field_name)

Source code in kiara/models/aliases/__init__.py

def get_value_obj(self, field_name: str) -> Value:

    item = self.get_child_map(field_name=field_name)
    if item is None:
        return self._data_registry.NONE_VALUE
    if item.assoc_value is None:
        raise Exception(f"No value associated for field '{field_name}'.")

    return self._data_registry.get_value(value_id=item.assoc_value)

print_tree(self)

Source code in kiara/models/aliases/__init__.py

def print_tree(self):

    t = self.get_tree("base")
    terminal_print(t)

set_alias(self, alias, value_id)

Source code in kiara/models/aliases/__init__.py

def set_alias(self, alias: str, value_id: Optional[uuid.UUID]) -> "AliasValueMap":

    if VALUE_ALIAS_SEPARATOR not in alias:
        child = None
        field_name: Optional[str] = alias
        rest = None
    else:
        child, rest = alias.split(VALUE_ALIAS_SEPARATOR, maxsplit=1)
        field_name = None

    if child is None:
        # means we are setting the alias in this map
        assert field_name is not None
        new_map = self._set_local_value_item(
            field_name=field_name, value_id=value_id
        )
        return new_map
    else:
        # means we are dealing with an intermediate alias map
        assert rest is not None
        assert child is not None
        assert field_name is None
        if child not in self.value_items.keys():
            if not self._auto_schema:
                raise Exception(
                    f"Can't set alias '{alias}', no schema set for field: '{child}'."
                )
            else:
                self.set_alias_schema(alias=child, schema=ValueSchema(type="any"))

        field_item: Optional[AliasValueMap] = None
        try:
            field_item = self.get_child_map(field_name=child)
        except KeyError:
            pass

        if self.alias:
            new_alias = f"{self.alias}.{child}"
        else:
            new_alias = child

        if field_item is None:
            new_version = 0
            schemas = {}
            self.value_items[child] = {}
        else:
            max_version = len(field_item.keys())
            new_version = max_version + 1
            assert field_item.alias == new_alias
            assert field_item.version == max_version
            schemas = field_item.values_schema

        new_map = AliasValueMap(
            alias=new_alias,
            version=new_version,
            assoc_schema=self.values_schema[child],
            assoc_value=None,
            values_schema=schemas,
        )
        new_map._data_registry = self._data_registry
        self.value_items[child][new_version] = new_map

        new_map.set_alias(alias=rest, value_id=value_id)

    return new_map

set_alias_schema(self, alias, schema)

Source code in kiara/models/aliases/__init__.py

def set_alias_schema(self, alias: str, schema: ValueSchema):

    if self._schema_locked:
        raise Exception(f"Can't add schema for alias '{alias}': schema locked.")

    if VALUE_ALIAS_SEPARATOR not in alias:

        self._set_local_field_schema(field_name=alias, schema=schema)
    else:
        child, rest = alias.split(VALUE_ALIAS_SEPARATOR, maxsplit=1)

        if child in self.values_schema.keys():
            child_map = self.get_child_map(child)
        else:
            self._set_local_field_schema(
                field_name=child, schema=ValueSchema(type="none")
            )
            child_map = self.set_alias(alias=child, value_id=None)

        assert child_map is not None

        child_map.set_alias_schema(alias=rest, schema=schema)

set_aliases(self, **aliases)

Source code in kiara/models/aliases/__init__.py

def set_aliases(self, **aliases) -> Mapping[str, "AliasValueMap"]:

    result = {}
    for k, v in aliases.items():
        r = self.set_alias(alias=k, value_id=v)
        result[k] = r

    return result

set_value(self, field_name, data)

Source code in kiara/models/aliases/__init__.py

def set_value(self, field_name: str, data: Any) -> None:

    assert VALUE_ALIAS_SEPARATOR not in field_name

    value = self._data_registry.register_data(data)
    self.set_alias(alias=field_name, value_id=value.value_id)

documentation

Classes

AuthorModel (BaseModel) pydantic-model

Source code in kiara/models/documentation.py

class AuthorModel(BaseModel):

    name: str = Field(description="The full name of the author.")
    email: Optional[EmailStr] = Field(
        description="The email address of the author", default=None
    )

Attributes

email: EmailStr pydantic-field

The email address of the author

name: str pydantic-field required

The full name of the author.

AuthorsMetadataModel (KiaraModel) pydantic-model

Source code in kiara/models/documentation.py

class AuthorsMetadataModel(KiaraModel):

    _kiara_model_id = "metadata.authors"

    class Config:
        extra = Extra.ignore

    _metadata_key = "origin"

    @classmethod
    def from_class(cls, item_cls: Type):

        data = get_metadata_for_python_module_or_class(item_cls)  # type: ignore
        merged = merge_dicts(*data)
        return cls.parse_obj(merged)

    authors: List[AuthorModel] = Field(
        description="The authors/creators of this item.", default_factory=list
    )

    def create_renderable(self, **config: Any) -> RenderableType:

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("Name")
        table.add_column("Email", style="i")

        for author in reversed(self.authors):
            if author.email:
                authors: Tuple[str, Union[str, EmailStr]] = (author.name, author.email)
            else:
                authors = (author.name, "")
            table.add_row(*authors)

        return table

Attributes

authors: List[kiara.models.documentation.AuthorModel] pydantic-field

The authors/creators of this item.

Config

Source code in kiara/models/documentation.py

class Config:
    extra = Extra.ignore

create_renderable(self, **config)

Source code in kiara/models/documentation.py

def create_renderable(self, **config: Any) -> RenderableType:

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("Name")
    table.add_column("Email", style="i")

    for author in reversed(self.authors):
        if author.email:
            authors: Tuple[str, Union[str, EmailStr]] = (author.name, author.email)
        else:
            authors = (author.name, "")
        table.add_row(*authors)

    return table

from_class(item_cls) classmethod

Source code in kiara/models/documentation.py

@classmethod
def from_class(cls, item_cls: Type):

    data = get_metadata_for_python_module_or_class(item_cls)  # type: ignore
    merged = merge_dicts(*data)
    return cls.parse_obj(merged)

ContextMetadataModel (KiaraModel) pydantic-model

Source code in kiara/models/documentation.py

class ContextMetadataModel(KiaraModel):

    _kiara_model_id = "metadata.context"

    class Config:
        extra = Extra.ignore

    @classmethod
    def from_class(cls, item_cls: Type):

        data = get_metadata_for_python_module_or_class(item_cls)  # type: ignore
        merged = merge_dicts(*data)
        return cls.parse_obj(merged)

    _metadata_key = "properties"

    references: Dict[str, LinkModel] = Field(
        description="References for the item.", default_factory=dict
    )
    tags: List[str] = Field(
        description="A list of tags for the item.", default_factory=list
    )
    labels: Dict[str, str] = Field(
        description="A list of labels for the item.", default_factory=list
    )

    def create_renderable(self, **config: Any) -> RenderableType:

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("Key", style="i")
        table.add_column("Value")

        if self.tags:
            table.add_row("Tags", ", ".join(self.tags))
        if self.labels:
            labels = []
            for k, v in self.labels.items():
                labels.append(f"[i]{k}[/i]: {v}")
            table.add_row("Labels", "\n".join(labels))

        if self.references:
            references = []
            for _k, _v in self.references.items():
                link = f"[link={_v.url}]{_v.url}[/link]"
                references.append(f"[i]{_k}[/i]: {link}")
            table.add_row("References", "\n".join(references))

        return table

    def add_reference(
        self,
        ref_type: str,
        url: str,
        desc: Optional[str] = None,
        force: bool = False,
    ):

        if ref_type in self.references.keys() and not force:
            raise Exception(f"Reference of type '{ref_type}' already present.")
        link = LinkModel(url=url, desc=desc)
        self.references[ref_type] = link

    def get_url_for_reference(self, ref: str) -> Optional[str]:

        link = self.references.get(ref, None)
        if not link:
            return None

        return link.url

Attributes

labels: Dict[str, str] pydantic-field

A list of labels for the item.

references: Dict[str, kiara.models.documentation.LinkModel] pydantic-field

References for the item.

tags: List[str] pydantic-field

A list of tags for the item.

Config

Source code in kiara/models/documentation.py

class Config:
    extra = Extra.ignore

add_reference(self, ref_type, url, desc=None, force=False)

Source code in kiara/models/documentation.py

def add_reference(
    self,
    ref_type: str,
    url: str,
    desc: Optional[str] = None,
    force: bool = False,
):

    if ref_type in self.references.keys() and not force:
        raise Exception(f"Reference of type '{ref_type}' already present.")
    link = LinkModel(url=url, desc=desc)
    self.references[ref_type] = link

create_renderable(self, **config)

Source code in kiara/models/documentation.py

def create_renderable(self, **config: Any) -> RenderableType:

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("Key", style="i")
    table.add_column("Value")

    if self.tags:
        table.add_row("Tags", ", ".join(self.tags))
    if self.labels:
        labels = []
        for k, v in self.labels.items():
            labels.append(f"[i]{k}[/i]: {v}")
        table.add_row("Labels", "\n".join(labels))

    if self.references:
        references = []
        for _k, _v in self.references.items():
            link = f"[link={_v.url}]{_v.url}[/link]"
            references.append(f"[i]{_k}[/i]: {link}")
        table.add_row("References", "\n".join(references))

    return table

from_class(item_cls) classmethod

Source code in kiara/models/documentation.py

@classmethod
def from_class(cls, item_cls: Type):

    data = get_metadata_for_python_module_or_class(item_cls)  # type: ignore
    merged = merge_dicts(*data)
    return cls.parse_obj(merged)

get_url_for_reference(self, ref)

Source code in kiara/models/documentation.py

def get_url_for_reference(self, ref: str) -> Optional[str]:

    link = self.references.get(ref, None)
    if not link:
        return None

    return link.url

DocumentationMetadataModel (KiaraModel) pydantic-model

Source code in kiara/models/documentation.py

class DocumentationMetadataModel(KiaraModel):

    _kiara_model_id = "metadata.documentation"

    _metadata_key = "documentation"

    @classmethod
    def from_class_doc(cls, item_cls: Type):

        doc = item_cls.__doc__

        if not doc:
            doc = DEFAULT_NO_DESC_VALUE

        doc = inspect.cleandoc(doc)
        return cls.from_string(doc)

    @classmethod
    def from_function(cls, func: Callable):

        doc = func.__doc__

        if not doc:
            doc = DEFAULT_NO_DESC_VALUE

        doc = inspect.cleandoc(doc)
        return cls.from_string(doc)

    @classmethod
    def from_string(cls, doc: Optional[str]):

        if not doc:
            doc = DEFAULT_NO_DESC_VALUE

        if "\n" in doc:
            desc, doc = doc.split("\n", maxsplit=1)
        else:
            desc = doc
            doc = None

        if doc:
            doc = doc.strip()

        return cls(description=desc.strip(), doc=doc)

    @classmethod
    def from_dict(cls, data: Mapping):

        doc = data.get("doc", None)
        desc = data.get("description", None)
        if desc is None:
            desc = data.get("desc", None)

        if not doc and not desc:
            return cls.from_string(DEFAULT_NO_DESC_VALUE)
        elif doc and not desc:
            return cls.from_string(doc)
        elif desc and not doc:
            return cls.from_string(desc)
        else:
            return cls(description=desc, doc=doc)

    @classmethod
    def create(cls, item: Any):

        if not item:
            return cls.from_string(DEFAULT_NO_DESC_VALUE)
        elif isinstance(item, DocumentationMetadataModel):
            return item
        elif isinstance(item, Mapping):
            return cls.from_dict(item)
        if isinstance(item, type):
            return cls.from_class_doc(item)
        elif isinstance(item, str):
            return cls.from_string(item)
        else:
            raise TypeError(f"Can't create documentation from type '{type(item)}'.")

    description: str = Field(
        description="Short description of the item.", default=DEFAULT_NO_DESC_VALUE
    )
    doc: Optional[str] = Field(
        description="Detailed documentation of the item (in markdown).", default=None
    )

    @property
    def is_set(self) -> bool:
        if self.description and self.description != DEFAULT_NO_DESC_VALUE:
            return True
        else:
            return False

    def _retrieve_data_to_hash(self) -> Any:
        return self.full_doc

    @property
    def full_doc(self):

        if self.doc:
            return f"{self.description}\n\n{self.doc}"
        else:
            return self.description

    def create_renderable(self, **config: Any) -> RenderableType:

        return Markdown(self.full_doc)

Attributes

description: str pydantic-field

Short description of the item.

doc: str pydantic-field

Detailed documentation of the item (in markdown).

full_doc property readonly

is_set: bool property readonly

create(item) classmethod

Source code in kiara/models/documentation.py

@classmethod
def create(cls, item: Any):

    if not item:
        return cls.from_string(DEFAULT_NO_DESC_VALUE)
    elif isinstance(item, DocumentationMetadataModel):
        return item
    elif isinstance(item, Mapping):
        return cls.from_dict(item)
    if isinstance(item, type):
        return cls.from_class_doc(item)
    elif isinstance(item, str):
        return cls.from_string(item)
    else:
        raise TypeError(f"Can't create documentation from type '{type(item)}'.")

create_renderable(self, **config)

Source code in kiara/models/documentation.py

def create_renderable(self, **config: Any) -> RenderableType:

    return Markdown(self.full_doc)

from_class_doc(item_cls) classmethod

Source code in kiara/models/documentation.py

@classmethod
def from_class_doc(cls, item_cls: Type):

    doc = item_cls.__doc__

    if not doc:
        doc = DEFAULT_NO_DESC_VALUE

    doc = inspect.cleandoc(doc)
    return cls.from_string(doc)

from_dict(data) classmethod

Source code in kiara/models/documentation.py

@classmethod
def from_dict(cls, data: Mapping):

    doc = data.get("doc", None)
    desc = data.get("description", None)
    if desc is None:
        desc = data.get("desc", None)

    if not doc and not desc:
        return cls.from_string(DEFAULT_NO_DESC_VALUE)
    elif doc and not desc:
        return cls.from_string(doc)
    elif desc and not doc:
        return cls.from_string(desc)
    else:
        return cls(description=desc, doc=doc)

from_function(func) classmethod

Source code in kiara/models/documentation.py

@classmethod
def from_function(cls, func: Callable):

    doc = func.__doc__

    if not doc:
        doc = DEFAULT_NO_DESC_VALUE

    doc = inspect.cleandoc(doc)
    return cls.from_string(doc)

from_string(doc) classmethod

Source code in kiara/models/documentation.py

@classmethod
def from_string(cls, doc: Optional[str]):

    if not doc:
        doc = DEFAULT_NO_DESC_VALUE

    if "\n" in doc:
        desc, doc = doc.split("\n", maxsplit=1)
    else:
        desc = doc
        doc = None

    if doc:
        doc = doc.strip()

    return cls(description=desc.strip(), doc=doc)

LinkModel (BaseModel) pydantic-model

Source code in kiara/models/documentation.py

class LinkModel(BaseModel):

    url: AnyUrl = Field(description="The url.")
    desc: Optional[str] = Field(
        description="A short description of the link content.",
        default=DEFAULT_NO_DESC_VALUE,
    )

Attributes

desc: str pydantic-field

A short description of the link content.

url: AnyUrl pydantic-field required

The url.

events special

Classes

KiaraEvent (BaseModel) pydantic-model

Source code in kiara/models/events/__init__.py

class KiaraEvent(BaseModel):
    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps

    def get_event_type(self) -> str:

        if hasattr(self, "event_type"):
            return self.event_type  # type: ignore

        name = camel_case_to_snake_case(self.__class__.__name__)
        return name

Config

Source code in kiara/models/events/__init__.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/models/events/__init__.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

get_event_type(self)

Source code in kiara/models/events/__init__.py

def get_event_type(self) -> str:

    if hasattr(self, "event_type"):
        return self.event_type  # type: ignore

    name = camel_case_to_snake_case(self.__class__.__name__)
    return name

RegistryEvent (KiaraEvent) pydantic-model

Source code in kiara/models/events/__init__.py

class RegistryEvent(KiaraEvent):

    kiara_id: uuid.UUID = Field(
        description="The id of the kiara context the value was created in."
    )

Attributes

kiara_id: UUID pydantic-field required

The id of the kiara context the value was created in.

Modules

alias_registry

Classes

AliasArchiveAddedEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/alias_registry.py

class AliasArchiveAddedEvent(RegistryEvent):

    event_type: Literal["alias_archive_added"] = "alias_archive_added"
    alias_archive_id: uuid.UUID = Field(
        description="The unique id of this data archive."
    )
    alias_archive_alias: str = Field(
        description="The alias this data archive was added as."
    )
    is_store: bool = Field(
        description="Whether this archive supports write operations (aka implements the 'DataStore' interface)."
    )
    is_default_store: bool = Field(
        description="Whether this store acts as default store."
    )

Attributes

alias_archive_alias: str pydantic-field required

The alias this data archive was added as.

alias_archive_id: UUID pydantic-field required

The unique id of this data archive.

event_type: Literal['alias_archive_added'] pydantic-field

is_default_store: bool pydantic-field required

Whether this store acts as default store.

is_store: bool pydantic-field required

Whether this archive supports write operations (aka implements the 'DataStore' interface).

AliasPreStoreEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/alias_registry.py

class AliasPreStoreEvent(RegistryEvent):

    event_type: Literal["alias_pre_store"] = "alias_pre_store"
    aliases: Iterable[str] = Field(description="The alias.")

Attributes

aliases: Iterable[str] pydantic-field required

The alias.

event_type: Literal['alias_pre_store'] pydantic-field

AliasStoredEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/alias_registry.py

class AliasStoredEvent(RegistryEvent):

    event_type: Literal["alias_stored"] = "alias_stored"
    alias: str = Field(description="The alias.")

Attributes

alias: str pydantic-field required

The alias.

event_type: Literal['alias_stored'] pydantic-field

data_registry

Classes

DataArchiveAddedEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/data_registry.py

class DataArchiveAddedEvent(RegistryEvent):

    event_type: Literal["data_archive_added"] = "data_archive_added"
    data_archive_id: uuid.UUID = Field(
        description="The unique id of this data archive."
    )
    data_archive_alias: str = Field(
        description="The alias this data archive was added as."
    )
    is_store: bool = Field(
        description="Whether this archive supports write operations (aka implements the 'DataStore' interface)."
    )
    is_default_store: bool = Field(
        description="Whether this store acts as default store."
    )

Attributes

data_archive_alias: str pydantic-field required

The alias this data archive was added as.

data_archive_id: UUID pydantic-field required

The unique id of this data archive.

event_type: Literal['data_archive_added'] pydantic-field

is_default_store: bool pydantic-field required

Whether this store acts as default store.

is_store: bool pydantic-field required

Whether this archive supports write operations (aka implements the 'DataStore' interface).

ValueCreatedEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/data_registry.py

class ValueCreatedEvent(RegistryEvent):

    event_type: Literal["value_created"] = "value_created"
    value: Value = Field(description="The value metadata.")

Attributes

event_type: Literal['value_created'] pydantic-field

value: Value pydantic-field required

The value metadata.

ValuePreStoreEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/data_registry.py

class ValuePreStoreEvent(RegistryEvent):

    event_type: Literal["value_pre_store"] = "value_pre_store"
    value: Value = Field(description="The value metadata.")

Attributes

event_type: Literal['value_pre_store'] pydantic-field

value: Value pydantic-field required

The value metadata.

ValueRegisteredEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/data_registry.py

class ValueRegisteredEvent(RegistryEvent):

    event_type: Literal["value_registered"] = "value_registered"
    value: Value = Field(description="The value metadata.")

Attributes

event_type: Literal['value_registered'] pydantic-field

value: Value pydantic-field required

The value metadata.

ValueStoredEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/data_registry.py

class ValueStoredEvent(RegistryEvent):

    event_type: Literal["value_stored"] = "value_stored"
    value: Value = Field(description="The value metadata.")

Attributes

event_type: Literal['value_stored'] pydantic-field

value: Value pydantic-field required

The value metadata.

job_registry

Classes

JobArchiveAddedEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/job_registry.py

class JobArchiveAddedEvent(RegistryEvent):

    event_type: Literal["job_archive_added"] = "job_archive_added"

    job_archive_id: uuid.UUID = Field(description="The unique id of this job archive.")
    job_archive_alias: str = Field(
        description="The alias this job archive was added as."
    )
    is_store: bool = Field(
        description="Whether this archive supports write operations (aka implements the 'JobStore' interface)."
    )
    is_default_store: bool = Field(
        description="Whether this store acts as default store."
    )

Attributes

event_type: Literal['job_archive_added'] pydantic-field

is_default_store: bool pydantic-field required

Whether this store acts as default store.

is_store: bool pydantic-field required

Whether this archive supports write operations (aka implements the 'JobStore' interface).

job_archive_alias: str pydantic-field required

The alias this job archive was added as.

job_archive_id: UUID pydantic-field required

The unique id of this job archive.

JobRecordPreStoreEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/job_registry.py

class JobRecordPreStoreEvent(RegistryEvent):

    event_type: Literal["job_record_pre_store"] = "job_record_pre_store"
    job_record: JobRecord = Field(description="The job record.")

Attributes

event_type: Literal['job_record_pre_store'] pydantic-field

job_record: JobRecord pydantic-field required

The job record.

JobRecordStoredEvent (RegistryEvent) pydantic-model

Source code in kiara/models/events/job_registry.py

class JobRecordStoredEvent(RegistryEvent):

    event_type: Literal["job_record_stored"] = "job_record_stored"
    job_record: JobRecord = Field(description="The job record.")

Attributes

event_type: Literal['job_record_stored'] pydantic-field

job_record: JobRecord pydantic-field required

The job record.

pipeline

Classes

ChangedValue (BaseModel) pydantic-model

Source code in kiara/models/events/pipeline.py

class ChangedValue(BaseModel):

    old: Optional[uuid.UUID]
    new: Optional[uuid.UUID]

new: UUID pydantic-field

old: UUID pydantic-field

PipelineDetails (BaseModel) pydantic-model

Source code in kiara/models/events/pipeline.py

class PipelineDetails(BaseModel):
    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps

    kiara_id: uuid.UUID = Field(description="The id of the kiara context.")
    pipeline_id: uuid.UUID = Field(description="The id of the pipeline.")

    pipeline_status: StepStatus = Field(
        description="The current status of this pipeline."
    )
    invalid_details: Dict[str, str] = Field(
        description="Details about fields that are invalid (if status < 'INPUTS_READY'.",
        default_factory=dict,
    )

    pipeline_inputs: Dict[str, uuid.UUID] = Field(
        description="The current pipeline inputs."
    )
    pipeline_outputs: Dict[str, uuid.UUID] = Field(
        description="The current pipeline outputs."
    )

    step_states: Dict[str, StepDetails] = Field(
        description="The state of each step within this pipeline."
    )

    def get_steps_by_processing_stage(self) -> MutableMapping[int, List[StepDetails]]:

        result: MutableMapping[int, List[StepDetails]] = SortedDict()
        for step_details in self.step_states.values():
            result.setdefault(step_details.processing_stage, []).append(step_details)
        return result

Attributes

invalid_details: Dict[str, str] pydantic-field

Details about fields that are invalid (if status < 'INPUTS_READY'.

kiara_id: UUID pydantic-field required

The id of the kiara context.

pipeline_id: UUID pydantic-field required

The id of the pipeline.

pipeline_inputs: Dict[str, uuid.UUID] pydantic-field required

The current pipeline inputs.

pipeline_outputs: Dict[str, uuid.UUID] pydantic-field required

The current pipeline outputs.

pipeline_status: StepStatus pydantic-field required

The current status of this pipeline.

step_states: Dict[str, kiara.models.events.pipeline.StepDetails] pydantic-field required

The state of each step within this pipeline.

Config

Source code in kiara/models/events/pipeline.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/models/events/pipeline.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

get_steps_by_processing_stage(self)

Source code in kiara/models/events/pipeline.py

def get_steps_by_processing_stage(self) -> MutableMapping[int, List[StepDetails]]:

    result: MutableMapping[int, List[StepDetails]] = SortedDict()
    for step_details in self.step_states.values():
        result.setdefault(step_details.processing_stage, []).append(step_details)
    return result

PipelineEvent (KiaraEvent) pydantic-model

Source code in kiara/models/events/pipeline.py

class PipelineEvent(KiaraEvent):
    @classmethod
    def create_event(
        cls,
        pipeline: "Pipeline",
        changed: Mapping[str, Mapping[str, Mapping[str, ChangedValue]]],
    ):

        pipeline_inputs = changed.get("__pipeline__", {}).get("inputs", {})
        pipeline_outputs = changed.get("__pipeline__", {}).get("outputs", {})

        step_inputs = {}
        step_outputs = {}

        invalidated_steps: Set[str] = set()

        for step_id, change_details in changed.items():
            if step_id == "__pipeline__":
                continue
            inputs = change_details.get("inputs", None)
            if inputs:
                invalidated_steps.add(step_id)
                step_inputs[step_id] = inputs
            outputs = change_details.get("outputs", None)
            if outputs:
                invalidated_steps.add(step_id)
                step_outputs[step_id] = outputs

        event = PipelineEvent(
            kiara_id=pipeline.kiara_id,
            pipeline_id=pipeline.pipeline_id,
            pipeline_inputs_changed=pipeline_inputs,
            pipeline_outputs_changed=pipeline_outputs,
            step_inputs_changed=step_inputs,
            step_outputs_changed=step_outputs,
            changed_steps=sorted(invalidated_steps),
        )
        return event

    class Config:
        allow_mutation = False

    kiara_id: uuid.UUID = Field(
        description="The id of the kiara context that created the pipeline."
    )
    pipeline_id: uuid.UUID = Field(description="The pipeline id.")

    pipeline_inputs_changed: Dict[str, ChangedValue] = Field(
        description="Details about changed pipeline input values.", default_factory=dict
    )
    pipeline_outputs_changed: Dict[str, ChangedValue] = Field(
        description="Details about changed pipeline output values.",
        default_factory=dict,
    )

    step_inputs_changed: Dict[str, Mapping[str, ChangedValue]] = Field(
        description="Details about changed step input values.", default_factory=dict
    )
    step_outputs_changed: Dict[str, Mapping[str, ChangedValue]] = Field(
        description="Details about changed step output values.", default_factory=dict
    )

    changed_steps: List[str] = Field(
        description="A list of all step ids that have newly invalidated outputs."
    )

    def __repr__(self):
        return f"{self.__class__.__name__}(pipeline_id={self.pipeline_id}, invalidated_steps={', '.join(self.changed_steps)})"

    def __str__(self):
        return self.__repr__()

Attributes

changed_steps: List[str] pydantic-field required

A list of all step ids that have newly invalidated outputs.

kiara_id: UUID pydantic-field required

The id of the kiara context that created the pipeline.

pipeline_id: UUID pydantic-field required

The pipeline id.

pipeline_inputs_changed: Dict[str, kiara.models.events.pipeline.ChangedValue] pydantic-field

Details about changed pipeline input values.

pipeline_outputs_changed: Dict[str, kiara.models.events.pipeline.ChangedValue] pydantic-field

Details about changed pipeline output values.

step_inputs_changed: Dict[str, Mapping[str, kiara.models.events.pipeline.ChangedValue]] pydantic-field

Details about changed step input values.

step_outputs_changed: Dict[str, Mapping[str, kiara.models.events.pipeline.ChangedValue]] pydantic-field

Details about changed step output values.

Config

Source code in kiara/models/events/pipeline.py

class Config:
    allow_mutation = False

create_event(pipeline, changed) classmethod

Source code in kiara/models/events/pipeline.py

@classmethod
def create_event(
    cls,
    pipeline: "Pipeline",
    changed: Mapping[str, Mapping[str, Mapping[str, ChangedValue]]],
):

    pipeline_inputs = changed.get("__pipeline__", {}).get("inputs", {})
    pipeline_outputs = changed.get("__pipeline__", {}).get("outputs", {})

    step_inputs = {}
    step_outputs = {}

    invalidated_steps: Set[str] = set()

    for step_id, change_details in changed.items():
        if step_id == "__pipeline__":
            continue
        inputs = change_details.get("inputs", None)
        if inputs:
            invalidated_steps.add(step_id)
            step_inputs[step_id] = inputs
        outputs = change_details.get("outputs", None)
        if outputs:
            invalidated_steps.add(step_id)
            step_outputs[step_id] = outputs

    event = PipelineEvent(
        kiara_id=pipeline.kiara_id,
        pipeline_id=pipeline.pipeline_id,
        pipeline_inputs_changed=pipeline_inputs,
        pipeline_outputs_changed=pipeline_outputs,
        step_inputs_changed=step_inputs,
        step_outputs_changed=step_outputs,
        changed_steps=sorted(invalidated_steps),
    )
    return event

StepDetails (BaseModel) pydantic-model

Source code in kiara/models/events/pipeline.py

class StepDetails(BaseModel):

    kiara_id: uuid.UUID = Field(description="The id of the kiara context.")
    pipeline_id: uuid.UUID = Field(description="The id of the pipeline.")
    step_id: str = Field(description="The id of the step.")
    processing_stage: int = Field(
        description="The execution stage where this step is executed."
    )
    status: StepStatus = Field(description="The current status of this step.")
    invalid_details: Dict[str, str] = Field(
        description="Details about fields that are invalid (if status < 'INPUTS_READY'.",
        default_factory=dict,
    )
    inputs: Dict[str, uuid.UUID] = Field(description="The current inputs of this step.")
    outputs: Dict[str, uuid.UUID] = Field(
        description="The current outputs of this step."
    )

    @validator("inputs")
    def replace_none_values_inputs(cls, value):

        result = {}
        for k, v in value.items():
            if v is None:
                v = NONE_VALUE_ID
            result[k] = v
        return result

    @validator("outputs")
    def replace_none_values_outputs(cls, value):

        result = {}
        for k, v in value.items():
            if v is None:
                v = NOT_SET_VALUE_ID
            result[k] = v
        return result

    def _retrieve_data_to_hash(self) -> Any:
        return f"{self.kiara_id}.{self.pipeline_id}.{self.step_id}"

    def _retrieve_id(self) -> str:
        return f"{self.kiara_id}.{self.pipeline_id}.{self.step_id}"

Attributes

inputs: Dict[str, uuid.UUID] pydantic-field required

The current inputs of this step.

invalid_details: Dict[str, str] pydantic-field

Details about fields that are invalid (if status < 'INPUTS_READY'.

kiara_id: UUID pydantic-field required

The id of the kiara context.

outputs: Dict[str, uuid.UUID] pydantic-field required

The current outputs of this step.

pipeline_id: UUID pydantic-field required

The id of the pipeline.

processing_stage: int pydantic-field required

The execution stage where this step is executed.

status: StepStatus pydantic-field required

The current status of this step.

step_id: str pydantic-field required

The id of the step.

replace_none_values_inputs(value) classmethod

Source code in kiara/models/events/pipeline.py

@validator("inputs")
def replace_none_values_inputs(cls, value):

    result = {}
    for k, v in value.items():
        if v is None:
            v = NONE_VALUE_ID
        result[k] = v
    return result

replace_none_values_outputs(value) classmethod

Source code in kiara/models/events/pipeline.py

@validator("outputs")
def replace_none_values_outputs(cls, value):

    result = {}
    for k, v in value.items():
        if v is None:
            v = NOT_SET_VALUE_ID
        result[k] = v
    return result

filesystem

FILE_BUNDLE_IMPORT_AVAILABLE_COLUMNS

logger

Classes

FileBundle (KiaraModel) pydantic-model

Describes properties for the 'file_bundle' value type.

Source code in kiara/models/filesystem.py

class FileBundle(KiaraModel):
    """Describes properties for the 'file_bundle' value type."""

    _kiara_model_id = "instance.data.file_bundle"

    @classmethod
    def import_folder(
        cls,
        source: str,
        bundle_name: Optional[str] = None,
        import_config: Union[None, Mapping[str, Any], FolderImportConfig] = None,
        import_time: Optional[datetime.datetime] = None,
    ) -> "FileBundle":

        if not source:
            raise ValueError("No source path provided.")

        if not os.path.exists(os.path.realpath(source)):
            raise ValueError(f"Path does not exist: {source}")

        if not os.path.isdir(os.path.realpath(source)):
            raise ValueError(f"Path is not a file: {source}")

        if source.endswith(os.path.sep):
            source = source[0:-1]

        abs_path = os.path.abspath(source)

        if import_config is None:
            _import_config = FolderImportConfig()
        elif isinstance(import_config, Mapping):
            _import_config = FolderImportConfig(**import_config)
        elif isinstance(import_config, FolderImportConfig):
            _import_config = import_config
        else:
            raise TypeError(
                f"Invalid type for folder import config: {type(import_config)}."
            )

        included_files: Dict[str, FileModel] = {}
        exclude_dirs = _import_config.exclude_dirs
        invalid_extensions = _import_config.exclude_files

        valid_extensions = _import_config.include_files

        if import_time:
            bundle_import_time = import_time
        else:
            bundle_import_time = datetime.datetime.now()  # TODO: timezone

        sum_size = 0

        def include_file(filename: str) -> bool:

            if invalid_extensions and any(
                filename.endswith(ext) for ext in invalid_extensions
            ):
                return False
            if not valid_extensions:
                return True
            else:
                return any(filename.endswith(ext) for ext in valid_extensions)

        for root, dirnames, filenames in os.walk(abs_path, topdown=True):

            if exclude_dirs:
                dirnames[:] = [d for d in dirnames if d not in exclude_dirs]

            for filename in [
                f
                for f in filenames
                if os.path.isfile(os.path.join(root, f)) and include_file(f)
            ]:

                full_path = os.path.join(root, filename)
                rel_path = os.path.relpath(full_path, abs_path)

                file_model = FileModel.load_file(
                    full_path, import_time=bundle_import_time
                )
                sum_size = sum_size + file_model.size
                included_files[rel_path] = file_model

        if bundle_name is None:
            bundle_name = os.path.basename(source)

        bundle = FileBundle.create_from_file_models(
            files=included_files,
            path=abs_path,
            bundle_name=bundle_name,
            sum_size=sum_size,
            import_time=bundle_import_time,
        )
        return bundle

    @classmethod
    def create_from_file_models(
        cls,
        files: Mapping[str, FileModel],
        bundle_name: str,
        path: Optional[str] = None,
        sum_size: Optional[int] = None,
        import_time: Optional[datetime.datetime] = None,
    ) -> "FileBundle":

        if import_time:
            bundle_import_time = import_time
        else:
            bundle_import_time = datetime.datetime.now()  # TODO: timezone

        result: Dict[str, Any] = {}

        result["included_files"] = files

        result["import_time"] = datetime.datetime.now().isoformat()
        result["number_of_files"] = len(files)
        result["bundle_name"] = bundle_name
        result["import_time"] = bundle_import_time

        if sum_size is None:
            sum_size = 0
            for f in files.values():
                sum_size = sum_size + f.size
        result["size"] = sum_size

        bundle = FileBundle(**result)
        bundle._path = path
        return bundle

    _file_bundle_hash: Optional[int] = PrivateAttr(default=None)

    bundle_name: str = Field(description="The name of this bundle.")
    import_time: datetime.datetime = Field(
        description="The time when the file bundle was imported."
    )
    number_of_files: int = Field(
        description="How many files are included in this bundle."
    )
    included_files: Dict[str, FileModel] = Field(
        description="A map of all the included files, incl. their properties. Uses the relative path of each file as key."
    )
    size: int = Field(description="The size of all files in this folder, combined.")
    _path: Optional[str] = PrivateAttr(default=None)

    @property
    def path(self) -> str:
        if self._path is None:
            # TODO: better explanation, offer remedy like copying into temp folder
            raise Exception(
                "File bundle path not set, it appears this bundle is comprised of symlinks only."
            )
        return self._path

    def _retrieve_id(self) -> str:
        return str(self.file_bundle_hash)

    # @property
    # def model_data_hash(self) -> int:
    #     return self.file_bundle_hash

    def _retrieve_data_to_hash(self) -> Any:

        return {
            "bundle_name": self.bundle_name,
            "included_files": {
                k: v.instance_cid for k, v in self.included_files.items()
            },
        }

    def get_relative_path(self, file: FileModel):
        return os.path.relpath(file.path, self.path)

    def read_text_file_contents(self, ignore_errors: bool = False) -> Mapping[str, str]:

        content_dict: Dict[str, str] = {}

        def read_file(rel_path: str, full_path: str):
            with open(full_path, encoding="utf-8") as f:
                try:
                    content = f.read()
                    content_dict[rel_path] = content  # type: ignore
                except Exception as e:
                    if ignore_errors:
                        log_message(f"Can't read file: {e}")
                        logger.warning("ignore.file", path=full_path, reason=str(e))
                    else:
                        raise Exception(f"Can't read file (as text) '{full_path}: {e}")

        # TODO: common ignore files and folders
        for rel_path, f in self.included_files.items():
            if f._path:
                path = f._path
            else:
                path = self.get_relative_path(f)
            read_file(rel_path=rel_path, full_path=path)

        return content_dict

    @property
    def file_bundle_hash(self) -> int:

        # TODO: use sha256?
        if self._file_bundle_hash is not None:
            return self._file_bundle_hash

        obj = {k: v.file_hash for k, v in self.included_files.items()}
        h = DeepHash(obj, hasher=KIARA_HASH_FUNCTION)

        self._file_bundle_hash = h[obj]
        return self._file_bundle_hash

    def copy_bundle(
        self, target_path: str, bundle_name: Optional[str] = None
    ) -> "FileBundle":

        if target_path == self.path:
            raise Exception(f"Target path and current path are the same: {target_path}")

        result = {}
        for rel_path, item in self.included_files.items():
            _target_path = os.path.join(target_path, rel_path)
            new_fm = item.copy_file(_target_path)
            result[rel_path] = new_fm

        if bundle_name is None:
            bundle_name = os.path.basename(target_path)

        fb = FileBundle.create_from_file_models(
            files=result,
            bundle_name=bundle_name,
            path=target_path,
            sum_size=self.size,
            import_time=self.import_time,
        )
        if self._file_bundle_hash is not None:
            fb._file_bundle_hash = self._file_bundle_hash

        return fb

    def create_renderable(self, **config: Any) -> RenderableType:

        show_bundle_hash = config.get("show_bundle_hash", False)

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("key")
        table.add_column("value", style="i")

        table.add_row("bundle name", self.bundle_name)
        table.add_row("import_time", str(self.import_time))
        table.add_row("number_of_files", str(self.number_of_files))
        table.add_row("size", str(self.size))
        if show_bundle_hash:
            table.add_row("bundle_hash", str(self.file_bundle_hash))

        content = self._create_content_table(**config)
        table.add_row("included files", content)

        return table

    def _create_content_table(self, **render_config: Any) -> Table:

        # show_content = render_config.get("show_content_preview", False)
        max_no_included_files = render_config.get("max_no_files", 40)

        table = Table(show_header=True, box=box.SIMPLE)
        table.add_column("(relative) path")
        table.add_column("size")
        # if show_content:
        #     table.add_column("content preview")

        if (
            max_no_included_files < 0
            or len(self.included_files) <= max_no_included_files
        ):
            for f, model in self.included_files.items():
                row = [f, str(model.size)]
                table.add_row(*row)
        else:
            files = list(self.included_files.keys())
            half = int((max_no_included_files - 1) / 2)
            head = files[0:half]
            tail = files[-1 * half :]  # noqa
            for rel_path in head:
                model = self.included_files[rel_path]
                row = [rel_path, str(model.size)]
                table.add_row(*row)
            table.add_row("   ... output skipped ...", "")
            table.add_row("   ... output skipped ...", "")
            for rel_path in tail:
                model = self.included_files[rel_path]
                row = [rel_path, str(model.size)]
                table.add_row(*row)

        return table

    def __repr__(self):
        return f"FileBundle(name={self.bundle_name})"

    def __str__(self):
        return self.__repr__()

Attributes

bundle_name: str pydantic-field required

The name of this bundle.

file_bundle_hash: int property readonly

import_time: datetime pydantic-field required

The time when the file bundle was imported.

included_files: Dict[str, kiara.models.filesystem.FileModel] pydantic-field required

A map of all the included files, incl. their properties. Uses the relative path of each file as key.

number_of_files: int pydantic-field required

How many files are included in this bundle.

path: str property readonly

size: int pydantic-field required

The size of all files in this folder, combined.

copy_bundle(self, target_path, bundle_name=None)

Source code in kiara/models/filesystem.py

def copy_bundle(
    self, target_path: str, bundle_name: Optional[str] = None
) -> "FileBundle":

    if target_path == self.path:
        raise Exception(f"Target path and current path are the same: {target_path}")

    result = {}
    for rel_path, item in self.included_files.items():
        _target_path = os.path.join(target_path, rel_path)
        new_fm = item.copy_file(_target_path)
        result[rel_path] = new_fm

    if bundle_name is None:
        bundle_name = os.path.basename(target_path)

    fb = FileBundle.create_from_file_models(
        files=result,
        bundle_name=bundle_name,
        path=target_path,
        sum_size=self.size,
        import_time=self.import_time,
    )
    if self._file_bundle_hash is not None:
        fb._file_bundle_hash = self._file_bundle_hash

    return fb

create_from_file_models(files, bundle_name, path=None, sum_size=None, import_time=None) classmethod

Source code in kiara/models/filesystem.py

@classmethod
def create_from_file_models(
    cls,
    files: Mapping[str, FileModel],
    bundle_name: str,
    path: Optional[str] = None,
    sum_size: Optional[int] = None,
    import_time: Optional[datetime.datetime] = None,
) -> "FileBundle":

    if import_time:
        bundle_import_time = import_time
    else:
        bundle_import_time = datetime.datetime.now()  # TODO: timezone

    result: Dict[str, Any] = {}

    result["included_files"] = files

    result["import_time"] = datetime.datetime.now().isoformat()
    result["number_of_files"] = len(files)
    result["bundle_name"] = bundle_name
    result["import_time"] = bundle_import_time

    if sum_size is None:
        sum_size = 0
        for f in files.values():
            sum_size = sum_size + f.size
    result["size"] = sum_size

    bundle = FileBundle(**result)
    bundle._path = path
    return bundle

create_renderable(self, **config)

Source code in kiara/models/filesystem.py

def create_renderable(self, **config: Any) -> RenderableType:

    show_bundle_hash = config.get("show_bundle_hash", False)

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("key")
    table.add_column("value", style="i")

    table.add_row("bundle name", self.bundle_name)
    table.add_row("import_time", str(self.import_time))
    table.add_row("number_of_files", str(self.number_of_files))
    table.add_row("size", str(self.size))
    if show_bundle_hash:
        table.add_row("bundle_hash", str(self.file_bundle_hash))

    content = self._create_content_table(**config)
    table.add_row("included files", content)

    return table

get_relative_path(self, file)

Source code in kiara/models/filesystem.py

def get_relative_path(self, file: FileModel):
    return os.path.relpath(file.path, self.path)

import_folder(source, bundle_name=None, import_config=None, import_time=None) classmethod

Source code in kiara/models/filesystem.py

@classmethod
def import_folder(
    cls,
    source: str,
    bundle_name: Optional[str] = None,
    import_config: Union[None, Mapping[str, Any], FolderImportConfig] = None,
    import_time: Optional[datetime.datetime] = None,
) -> "FileBundle":

    if not source:
        raise ValueError("No source path provided.")

    if not os.path.exists(os.path.realpath(source)):
        raise ValueError(f"Path does not exist: {source}")

    if not os.path.isdir(os.path.realpath(source)):
        raise ValueError(f"Path is not a file: {source}")

    if source.endswith(os.path.sep):
        source = source[0:-1]

    abs_path = os.path.abspath(source)

    if import_config is None:
        _import_config = FolderImportConfig()
    elif isinstance(import_config, Mapping):
        _import_config = FolderImportConfig(**import_config)
    elif isinstance(import_config, FolderImportConfig):
        _import_config = import_config
    else:
        raise TypeError(
            f"Invalid type for folder import config: {type(import_config)}."
        )

    included_files: Dict[str, FileModel] = {}
    exclude_dirs = _import_config.exclude_dirs
    invalid_extensions = _import_config.exclude_files

    valid_extensions = _import_config.include_files

    if import_time:
        bundle_import_time = import_time
    else:
        bundle_import_time = datetime.datetime.now()  # TODO: timezone

    sum_size = 0

    def include_file(filename: str) -> bool:

        if invalid_extensions and any(
            filename.endswith(ext) for ext in invalid_extensions
        ):
            return False
        if not valid_extensions:
            return True
        else:
            return any(filename.endswith(ext) for ext in valid_extensions)

    for root, dirnames, filenames in os.walk(abs_path, topdown=True):

        if exclude_dirs:
            dirnames[:] = [d for d in dirnames if d not in exclude_dirs]

        for filename in [
            f
            for f in filenames
            if os.path.isfile(os.path.join(root, f)) and include_file(f)
        ]:

            full_path = os.path.join(root, filename)
            rel_path = os.path.relpath(full_path, abs_path)

            file_model = FileModel.load_file(
                full_path, import_time=bundle_import_time
            )
            sum_size = sum_size + file_model.size
            included_files[rel_path] = file_model

    if bundle_name is None:
        bundle_name = os.path.basename(source)

    bundle = FileBundle.create_from_file_models(
        files=included_files,
        path=abs_path,
        bundle_name=bundle_name,
        sum_size=sum_size,
        import_time=bundle_import_time,
    )
    return bundle

read_text_file_contents(self, ignore_errors=False)

Source code in kiara/models/filesystem.py

def read_text_file_contents(self, ignore_errors: bool = False) -> Mapping[str, str]:

    content_dict: Dict[str, str] = {}

    def read_file(rel_path: str, full_path: str):
        with open(full_path, encoding="utf-8") as f:
            try:
                content = f.read()
                content_dict[rel_path] = content  # type: ignore
            except Exception as e:
                if ignore_errors:
                    log_message(f"Can't read file: {e}")
                    logger.warning("ignore.file", path=full_path, reason=str(e))
                else:
                    raise Exception(f"Can't read file (as text) '{full_path}: {e}")

    # TODO: common ignore files and folders
    for rel_path, f in self.included_files.items():
        if f._path:
            path = f._path
        else:
            path = self.get_relative_path(f)
        read_file(rel_path=rel_path, full_path=path)

    return content_dict

FileModel (KiaraModel) pydantic-model

Describes properties for the 'file' value type.

Source code in kiara/models/filesystem.py

class FileModel(KiaraModel):
    """Describes properties for the 'file' value type."""

    _kiara_model_id = "instance.data.file"

    @classmethod
    def load_file(
        cls,
        source: str,
        file_name: Optional[str] = None,
        import_time: Optional[datetime.datetime] = None,
    ):
        """Utility method to read metadata of a file from disk and optionally move it into a data archive location."""

        import filetype
        import mimetypes

        if not source:
            raise ValueError("No source path provided.")

        if not os.path.exists(os.path.realpath(source)):
            raise ValueError(f"Path does not exist: {source}")

        if not os.path.isfile(os.path.realpath(source)):
            raise ValueError(f"Path is not a file: {source}")

        if file_name is None:
            file_name = os.path.basename(source)

        path: str = os.path.abspath(source)
        if import_time:
            file_import_time = import_time
        else:
            file_import_time = datetime.datetime.now()  # TODO: timezone

        file_stats = os.stat(path)
        size = file_stats.st_size

        r = mimetypes.guess_type(path)
        if r[0] is not None:
            mime_type = r[0]
        else:
            _mime_type = filetype.guess(path)
            if not _mime_type:
                mime_type = "application/octet-stream"
            else:
                mime_type = _mime_type.MIME

        m = FileModel(
            import_time=file_import_time,
            mime_type=mime_type,
            size=size,
            file_name=file_name,
        )
        m._path = path
        return m

    import_time: datetime.datetime = Field(
        description="The time when the file was imported."
    )
    mime_type: str = Field(description="The mime type of the file.")
    file_name: str = Field("The name of the file.")
    size: int = Field(description="The size of the file.")

    _path: Optional[str] = PrivateAttr(default=None)
    _file_hash: Optional[str] = PrivateAttr(default=None)
    _file_cid: Optional[CID] = PrivateAttr(default=None)

    # @validator("path")
    # def ensure_abs_path(cls, value):
    #     return os.path.abspath(value)

    @property
    def path(self) -> str:
        if self._path is None:
            raise Exception("File path not set for file model.")
        return self._path

    def _retrieve_data_to_hash(self) -> Any:
        data = {
            "file_name": self.file_name,
            "file_cid": self.file_cid,
        }
        return data

    # def get_id(self) -> str:
    #     return self.path

    def get_category_alias(self) -> str:
        return "instance.file_model"

    def copy_file(self, target: str, new_name: Optional[str] = None) -> "FileModel":

        target_path: str = os.path.abspath(target)
        os.makedirs(os.path.dirname(target_path), exist_ok=True)

        shutil.copy2(self.path, target_path)
        fm = FileModel.load_file(
            target, file_name=new_name, import_time=self.import_time
        )

        if self._file_hash is not None:
            fm._file_hash = self._file_hash

        return fm

    @property
    def file_hash(self) -> str:

        if self._file_hash is not None:
            return self._file_hash

        self._file_hash = str(self.file_cid)
        return self._file_hash

    @property
    def file_cid(self) -> CID:

        if self._file_cid is not None:
            return self._file_cid

        # TODO: auto-set codec?
        self._file_cid = compute_cid_from_file(file=self.path, codec="raw")
        return self._file_cid

    @property
    def file_name_without_extension(self) -> str:

        return self.file_name.split(".")[0]

    def read_text(self, max_lines: int = -1) -> str:
        """Read the content of a file."""

        with open(self.path, "rt") as f:
            if max_lines <= 0:
                content = f.read()
            else:
                content = "".join((next(f) for x in range(max_lines)))
        return content

    def read_bytes(self, length: int = -1) -> bytes:
        """Read the content of a file."""

        with open(self.path, "rb") as f:
            if length <= 0:
                content = f.read()
            else:
                content = f.read(length)
        return content

    def __repr__(self):
        return f"FileModel(name={self.file_name})"

    def __str__(self):
        return self.__repr__()

Attributes

file_cid: CID property readonly

file_hash: str property readonly

file_name: str pydantic-field

file_name_without_extension: str property readonly

import_time: datetime pydantic-field required

The time when the file was imported.

mime_type: str pydantic-field required

The mime type of the file.

path: str property readonly

size: int pydantic-field required

The size of the file.

Methods

copy_file(self, target, new_name=None)

Source code in kiara/models/filesystem.py

def copy_file(self, target: str, new_name: Optional[str] = None) -> "FileModel":

    target_path: str = os.path.abspath(target)
    os.makedirs(os.path.dirname(target_path), exist_ok=True)

    shutil.copy2(self.path, target_path)
    fm = FileModel.load_file(
        target, file_name=new_name, import_time=self.import_time
    )

    if self._file_hash is not None:
        fm._file_hash = self._file_hash

    return fm

get_category_alias(self)

Source code in kiara/models/filesystem.py

def get_category_alias(self) -> str:
    return "instance.file_model"

load_file(source, file_name=None, import_time=None) classmethod

Utility method to read metadata of a file from disk and optionally move it into a data archive location.

Source code in kiara/models/filesystem.py

@classmethod
def load_file(
    cls,
    source: str,
    file_name: Optional[str] = None,
    import_time: Optional[datetime.datetime] = None,
):
    """Utility method to read metadata of a file from disk and optionally move it into a data archive location."""

    import filetype
    import mimetypes

    if not source:
        raise ValueError("No source path provided.")

    if not os.path.exists(os.path.realpath(source)):
        raise ValueError(f"Path does not exist: {source}")

    if not os.path.isfile(os.path.realpath(source)):
        raise ValueError(f"Path is not a file: {source}")

    if file_name is None:
        file_name = os.path.basename(source)

    path: str = os.path.abspath(source)
    if import_time:
        file_import_time = import_time
    else:
        file_import_time = datetime.datetime.now()  # TODO: timezone

    file_stats = os.stat(path)
    size = file_stats.st_size

    r = mimetypes.guess_type(path)
    if r[0] is not None:
        mime_type = r[0]
    else:
        _mime_type = filetype.guess(path)
        if not _mime_type:
            mime_type = "application/octet-stream"
        else:
            mime_type = _mime_type.MIME

    m = FileModel(
        import_time=file_import_time,
        mime_type=mime_type,
        size=size,
        file_name=file_name,
    )
    m._path = path
    return m

read_bytes(self, length=-1)

Read the content of a file.

Source code in kiara/models/filesystem.py

def read_bytes(self, length: int = -1) -> bytes:
    """Read the content of a file."""

    with open(self.path, "rb") as f:
        if length <= 0:
            content = f.read()
        else:
            content = f.read(length)
    return content

read_text(self, max_lines=-1)

Read the content of a file.

Source code in kiara/models/filesystem.py

def read_text(self, max_lines: int = -1) -> str:
    """Read the content of a file."""

    with open(self.path, "rt") as f:
        if max_lines <= 0:
            content = f.read()
        else:
            content = "".join((next(f) for x in range(max_lines)))
    return content

FolderImportConfig (BaseModel) pydantic-model

Source code in kiara/models/filesystem.py

class FolderImportConfig(BaseModel):

    include_files: Optional[List[str]] = Field(
        description="A list of strings, include all files where the filename ends with that string.",
        default=None,
    )
    exclude_dirs: Optional[List[str]] = Field(
        description="A list of strings, exclude all folders whose name ends with that string.",
        default=None,
    )
    exclude_files: Optional[List[str]] = Field(
        description=f"A list of strings, exclude all files that match those (takes precedence over 'include_files'). Defaults to: {DEFAULT_EXCLUDE_FILES}.",
        default=DEFAULT_EXCLUDE_FILES,
    )

Attributes

exclude_dirs: List[str] pydantic-field

A list of strings, exclude all folders whose name ends with that string.

exclude_files: List[str] pydantic-field

A list of strings, exclude all files that match those (takes precedence over 'include_files'). Defaults to: ['.DS_Store'].

include_files: List[str] pydantic-field

A list of strings, include all files where the filename ends with that string.

info

INFO_BASE_CLASS

INFO_CLASS

Classes

InfoModelGroup (KiaraModel, Mapping, Generic) pydantic-model

Source code in kiara/models/info.py

class InfoModelGroup(KiaraModel, Mapping[str, ItemInfo]):
    @classmethod
    @abc.abstractmethod
    def base_info_class(cls) -> Type[ItemInfo]:
        pass

    # group_id: uuid.UUID = Field(
    #     description="The unique group id.", default_factory=ID_REGISTRY.generate
    # )
    group_alias: Optional[str] = Field(description="The group alias.", default=None)

    # def _retrieve_id(self) -> str:
    #     return str(self.group_id)

    def _retrieve_subcomponent_keys(self) -> Iterable[str]:
        return self.type_infos.keys()  # type: ignore

    def _retrieve_data_to_hash(self) -> Any:
        return {"type_name": self.type_name, "included_types": list(self.type_infos.keys())}  # type: ignore

    def get_type_infos(self) -> Mapping[str, ItemInfo]:
        return self.type_infos  # type: ignore

    def create_renderable(self, **config: Any) -> RenderableType:

        full_doc = config.get("full_doc", False)

        table = Table(show_header=True, box=box.SIMPLE, show_lines=full_doc)
        table.add_column("Type name", style="i")
        table.add_column("Description")

        for type_name in sorted(self.type_infos.keys()):  # type: ignore
            t_md = self.type_infos[type_name]  # type: ignore
            if full_doc:
                md = Markdown(t_md.documentation.full_doc)
            else:
                md = Markdown(t_md.documentation.description)
            table.add_row(type_name, md)

        return table

    def __getitem__(self, item: str) -> ItemInfo:

        return self.get_type_infos()[item]

    def __iter__(self):
        return iter(self.get_type_infos())

    def __len__(self):
        return len(self.get_type_infos())

Attributes

group_alias: str pydantic-field

The group alias.

base_info_class() classmethod

Source code in kiara/models/info.py

@classmethod
@abc.abstractmethod
def base_info_class(cls) -> Type[ItemInfo]:
    pass

create_renderable(self, **config)

Source code in kiara/models/info.py

def create_renderable(self, **config: Any) -> RenderableType:

    full_doc = config.get("full_doc", False)

    table = Table(show_header=True, box=box.SIMPLE, show_lines=full_doc)
    table.add_column("Type name", style="i")
    table.add_column("Description")

    for type_name in sorted(self.type_infos.keys()):  # type: ignore
        t_md = self.type_infos[type_name]  # type: ignore
        if full_doc:
            md = Markdown(t_md.documentation.full_doc)
        else:
            md = Markdown(t_md.documentation.description)
        table.add_row(type_name, md)

    return table

get_type_infos(self)

Source code in kiara/models/info.py

def get_type_infos(self) -> Mapping[str, ItemInfo]:
    return self.type_infos  # type: ignore

ItemInfo (KiaraModel) pydantic-model

Base class that holds/manages information about an item within kiara.

Source code in kiara/models/info.py

class ItemInfo(KiaraModel):
    """Base class that holds/manages information about an item within kiara."""

    @classmethod
    @abc.abstractmethod
    def category_name(cls) -> str:
        pass

    @validator("documentation", pre=True)
    def validate_doc(cls, value):

        return DocumentationMetadataModel.create(value)

    type_name: str = Field(description="The registered name for this item type.")
    documentation: DocumentationMetadataModel = Field(
        description="Documentation for the module."
    )
    authors: AuthorsMetadataModel = Field(
        description="Information about authorship for the module type."
    )
    context: ContextMetadataModel = Field(
        description="Generic properties of this module (description, tags, labels, references, ...)."
    )

    def _retrieve_id(self) -> str:
        return self.type_name

    def _retrieve_data_to_hash(self) -> Any:
        return self.type_name

    def create_renderable(self, **config: Any) -> RenderableType:

        include_doc = config.get("include_doc", True)

        table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
        table.add_column("property", style="i")
        table.add_column("value")

        if include_doc:
            table.add_row(
                "Documentation",
                Panel(self.documentation.create_renderable(), box=box.SIMPLE),
            )
        table.add_row("Author(s)", self.authors.create_renderable())
        table.add_row("Context", self.context.create_renderable())

        if hasattr(self, "python_class"):
            table.add_row("Python class", self.python_class.create_renderable())  # type: ignore

        return table

Attributes

authors: AuthorsMetadataModel pydantic-field required

Information about authorship for the module type.

context: ContextMetadataModel pydantic-field required

Generic properties of this module (description, tags, labels, references, ...).

documentation: DocumentationMetadataModel pydantic-field required

Documentation for the module.

type_name: str pydantic-field required

The registered name for this item type.

category_name() classmethod

Source code in kiara/models/info.py

@classmethod
@abc.abstractmethod
def category_name(cls) -> str:
    pass

create_renderable(self, **config)

Source code in kiara/models/info.py

def create_renderable(self, **config: Any) -> RenderableType:

    include_doc = config.get("include_doc", True)

    table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
    table.add_column("property", style="i")
    table.add_column("value")

    if include_doc:
        table.add_row(
            "Documentation",
            Panel(self.documentation.create_renderable(), box=box.SIMPLE),
        )
    table.add_row("Author(s)", self.authors.create_renderable())
    table.add_row("Context", self.context.create_renderable())

    if hasattr(self, "python_class"):
        table.add_row("Python class", self.python_class.create_renderable())  # type: ignore

    return table

validate_doc(value) classmethod

Source code in kiara/models/info.py

@validator("documentation", pre=True)
def validate_doc(cls, value):

    return DocumentationMetadataModel.create(value)

KiaraModelClassesInfo (TypeInfoModelGroup) pydantic-model

Source code in kiara/models/info.py

class KiaraModelClassesInfo(TypeInfoModelGroup):

    _kiara_model_id = "info.kiara_models"

    @classmethod
    def base_info_class(cls) -> Type[TypeInfo]:
        return KiaraModelTypeInfo

    type_name: Literal["kiara_model"] = "kiara_model"
    type_infos: Mapping[str, KiaraModelTypeInfo] = Field(
        description="The value metadata info instances for each type."
    )

Attributes

type_infos: Mapping[str, kiara.models.info.KiaraModelTypeInfo] pydantic-field required

The value metadata info instances for each type.

type_name: Literal['kiara_model'] pydantic-field

base_info_class() classmethod

Source code in kiara/models/info.py

@classmethod
def base_info_class(cls) -> Type[TypeInfo]:
    return KiaraModelTypeInfo

KiaraModelTypeInfo (TypeInfo) pydantic-model

Source code in kiara/models/info.py

class KiaraModelTypeInfo(TypeInfo):

    _kiara_model_id = "info.kiara_model"

    @classmethod
    def create_from_type_class(
        self, type_cls: Type[KiaraModel]
    ) -> "KiaraModelTypeInfo":

        authors_md = AuthorsMetadataModel.from_class(type_cls)
        doc = DocumentationMetadataModel.from_class_doc(type_cls)
        python_class = PythonClass.from_class(type_cls)
        properties_md = ContextMetadataModel.from_class(type_cls)
        type_name = type_cls._kiara_model_id  # type: ignore
        schema = type_cls.schema()

        return KiaraModelTypeInfo.construct(
            type_name=type_name,
            documentation=doc,
            authors=authors_md,
            context=properties_md,
            python_class=python_class,
            metadata_schema=schema,
        )

    @classmethod
    def base_class(self) -> Type[KiaraModel]:
        return KiaraModel

    @classmethod
    def category_name(cls) -> str:
        return "info.kiara_model"

    metadata_schema: Dict[str, Any] = Field(
        description="The (json) schema for this model data."
    )

    def create_renderable(self, **config: Any) -> RenderableType:

        include_doc = config.get("include_doc", True)
        include_schema = config.get("include_schema", True)

        table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
        table.add_column("property", style="i")
        table.add_column("value")

        if include_doc:
            table.add_row(
                "Documentation",
                Panel(self.documentation.create_renderable(), box=box.SIMPLE),
            )
        table.add_row("Author(s)", self.authors.create_renderable())
        table.add_row("Context", self.context.create_renderable())

        if hasattr(self, "python_class"):
            table.add_row("Python class", self.python_class.create_renderable())

        if include_schema:
            schema = Syntax(
                orjson_dumps(self.metadata_schema, option=orjson.OPT_INDENT_2),
                "json",
                background_color="default",
            )
            table.add_row("metadata_schema", schema)

        return table

Attributes

metadata_schema: Dict[str, Any] pydantic-field required

The (json) schema for this model data.

base_class() classmethod

Source code in kiara/models/info.py

@classmethod
def base_class(self) -> Type[KiaraModel]:
    return KiaraModel

category_name() classmethod

Source code in kiara/models/info.py

@classmethod
def category_name(cls) -> str:
    return "info.kiara_model"

create_from_type_class(type_cls) classmethod

Source code in kiara/models/info.py

@classmethod
def create_from_type_class(
    self, type_cls: Type[KiaraModel]
) -> "KiaraModelTypeInfo":

    authors_md = AuthorsMetadataModel.from_class(type_cls)
    doc = DocumentationMetadataModel.from_class_doc(type_cls)
    python_class = PythonClass.from_class(type_cls)
    properties_md = ContextMetadataModel.from_class(type_cls)
    type_name = type_cls._kiara_model_id  # type: ignore
    schema = type_cls.schema()

    return KiaraModelTypeInfo.construct(
        type_name=type_name,
        documentation=doc,
        authors=authors_md,
        context=properties_md,
        python_class=python_class,
        metadata_schema=schema,
    )

create_renderable(self, **config)

Source code in kiara/models/info.py

def create_renderable(self, **config: Any) -> RenderableType:

    include_doc = config.get("include_doc", True)
    include_schema = config.get("include_schema", True)

    table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
    table.add_column("property", style="i")
    table.add_column("value")

    if include_doc:
        table.add_row(
            "Documentation",
            Panel(self.documentation.create_renderable(), box=box.SIMPLE),
        )
    table.add_row("Author(s)", self.authors.create_renderable())
    table.add_row("Context", self.context.create_renderable())

    if hasattr(self, "python_class"):
        table.add_row("Python class", self.python_class.create_renderable())

    if include_schema:
        schema = Syntax(
            orjson_dumps(self.metadata_schema, option=orjson.OPT_INDENT_2),
            "json",
            background_color="default",
        )
        table.add_row("metadata_schema", schema)

    return table

TypeInfo (ItemInfo, Generic) pydantic-model

Source code in kiara/models/info.py

class TypeInfo(ItemInfo, Generic[INFO_BASE_CLASS]):
    @classmethod
    @abc.abstractmethod
    def create_from_type_class(self, type_cls: Type[INFO_BASE_CLASS]) -> "ItemInfo":
        pass

    @classmethod
    @abc.abstractmethod
    def base_class(self) -> Type[INFO_BASE_CLASS]:
        pass

    python_class: PythonClass = Field(
        description="The python class that implements this module type."
    )

Attributes

python_class: PythonClass pydantic-field required

The python class that implements this module type.

base_class() classmethod

Source code in kiara/models/info.py

@classmethod
@abc.abstractmethod
def base_class(self) -> Type[INFO_BASE_CLASS]:
    pass

create_from_type_class(type_cls) classmethod

Source code in kiara/models/info.py

@classmethod
@abc.abstractmethod
def create_from_type_class(self, type_cls: Type[INFO_BASE_CLASS]) -> "ItemInfo":
    pass

TypeInfoModelGroup (InfoModelGroup, Mapping, Generic) pydantic-model

Source code in kiara/models/info.py

class TypeInfoModelGroup(InfoModelGroup, Mapping[str, TypeInfo]):
    @classmethod
    @abc.abstractmethod
    def base_info_class(cls) -> Type[TypeInfo]:
        pass

    @classmethod
    def create_from_type_items(
        cls, group_alias: Optional[str] = None, **items: Type
    ) -> "TypeInfoModelGroup":

        type_infos = {
            k: cls.base_info_class().create_from_type_class(v) for k, v in items.items()
        }
        data_types_info = cls.construct(group_alias=group_alias, type_infos=type_infos)  # type: ignore
        return data_types_info

    def get_type_infos(self) -> Mapping[str, TypeInfo]:
        return self.type_infos  # type: ignore

    def __getitem__(self, item: str) -> TypeInfo:

        return self.get_type_infos()[item]

    def __iter__(self):
        return iter(self.get_type_infos())

    def __len__(self):
        return len(self.get_type_infos())

base_info_class() classmethod

Source code in kiara/models/info.py

@classmethod
@abc.abstractmethod
def base_info_class(cls) -> Type[TypeInfo]:
    pass

create_from_type_items(group_alias=None, **items) classmethod

Source code in kiara/models/info.py

@classmethod
def create_from_type_items(
    cls, group_alias: Optional[str] = None, **items: Type
) -> "TypeInfoModelGroup":

    type_infos = {
        k: cls.base_info_class().create_from_type_class(v) for k, v in items.items()
    }
    data_types_info = cls.construct(group_alias=group_alias, type_infos=type_infos)  # type: ignore
    return data_types_info

get_type_infos(self)

Source code in kiara/models/info.py

def get_type_infos(self) -> Mapping[str, TypeInfo]:
    return self.type_infos  # type: ignore

find_kiara_models(alias=None, only_for_package=None)

Source code in kiara/models/info.py

def find_kiara_models(
    alias: Optional[str] = None, only_for_package: Optional[str] = None
) -> KiaraModelClassesInfo:

    models = find_all_kiara_model_classes()

    group: KiaraModelClassesInfo = KiaraModelClassesInfo.create_from_type_items(group_alias=alias, **models)  # type: ignore

    if only_for_package:
        temp = {}
        for key, info in group.items():
            if info.context.labels.get("package") == only_for_package:
                temp[key] = info

        group = KiaraModelClassesInfo.construct(
            group_id=group.instance_id, group_alias=group.group_alias, type_infos=temp  # type: ignore
        )

    return group

module special

Classes

KiaraModuleClass (PythonClass) pydantic-model

Source code in kiara/models/module/__init__.py

class KiaraModuleClass(PythonClass):

    _kiara_model_id: str = "metadata.kiara_module_class"

    @classmethod
    def from_module(cls, module: "KiaraModule"):

        item_cls = module.__class__

        cls_name = item_cls.__name__
        module_name = item_cls.__module__
        if module_name == "builtins":
            full_name = cls_name
        else:
            full_name = f"{item_cls.__module__}.{item_cls.__name__}"

        conf: Dict[str, Any] = {
            "python_class_name": cls_name,
            "python_module_name": module_name,
            "full_name": full_name,
        }

        conf["module_config"] = module.config
        conf["inputs_schema"] = module.inputs_schema
        conf["outputs_schema"] = module.outputs_schema

        result = KiaraModuleClass.construct(**conf)
        result._cls_cache = item_cls
        result._module_instance_cache = module
        return result

    module_config: Dict[str, Any] = Field(description="The module config.")
    inputs_schema: Dict[str, ValueSchema] = Field(
        description="The schema for the module input(s)."
    )
    outputs_schema: Dict[str, ValueSchema] = Field(
        description="The schema for the module output(s)."
    )

    _module_instance_cache: "KiaraModule" = PrivateAttr(default=None)

    def get_kiara_module_instance(self) -> "KiaraModule":

        if self._module_instance_cache is not None:
            return self._module_instance_cache

        m_cls = self.get_class()
        self._module_instance_cache = m_cls(module_config=self.module_config)
        return self._module_instance_cache

Attributes

inputs_schema: Dict[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

The schema for the module input(s).

module_config: Dict[str, Any] pydantic-field required

The module config.

outputs_schema: Dict[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

The schema for the module output(s).

from_module(module) classmethod

Source code in kiara/models/module/__init__.py

@classmethod
def from_module(cls, module: "KiaraModule"):

    item_cls = module.__class__

    cls_name = item_cls.__name__
    module_name = item_cls.__module__
    if module_name == "builtins":
        full_name = cls_name
    else:
        full_name = f"{item_cls.__module__}.{item_cls.__name__}"

    conf: Dict[str, Any] = {
        "python_class_name": cls_name,
        "python_module_name": module_name,
        "full_name": full_name,
    }

    conf["module_config"] = module.config
    conf["inputs_schema"] = module.inputs_schema
    conf["outputs_schema"] = module.outputs_schema

    result = KiaraModuleClass.construct(**conf)
    result._cls_cache = item_cls
    result._module_instance_cache = module
    return result

get_kiara_module_instance(self)

Source code in kiara/models/module/__init__.py

def get_kiara_module_instance(self) -> "KiaraModule":

    if self._module_instance_cache is not None:
        return self._module_instance_cache

    m_cls = self.get_class()
    self._module_instance_cache = m_cls(module_config=self.module_config)
    return self._module_instance_cache

KiaraModuleConfig (KiaraModel) pydantic-model

Base class that describes the configuration a [KiaraModule][kiara.module.KiaraModule] class accepts.

This is stored in the _config_cls class attribute in each KiaraModule class.

There are two config options every KiaraModule supports:

• constants, and
• defaults

Constants are pre-set inputs, and users can't change them and an error is thrown if they try. Defaults are default values that override the schema defaults, and those can be overwritten by users. If both a constant and a default value is set for an input field, an error is thrown.

Source code in kiara/models/module/__init__.py

class KiaraModuleConfig(KiaraModel):
    """Base class that describes the configuration a [``KiaraModule``][kiara.module.KiaraModule] class accepts.

    This is stored in the ``_config_cls`` class attribute in each ``KiaraModule`` class.

    There are two config options every ``KiaraModule`` supports:

     - ``constants``, and
     - ``defaults``

     Constants are pre-set inputs, and users can't change them and an error is thrown if they try. Defaults are default
     values that override the schema defaults, and those can be overwritten by users. If both a constant and a default
     value is set for an input field, an error is thrown.
    """

    _kiara_model_id = "instance.module_config"

    @classmethod
    def requires_config(cls, config: Optional[Mapping[str, Any]] = None) -> bool:
        """Return whether this class can be used as-is, or requires configuration before an instance can be created."""

        for field_name, field in cls.__fields__.items():
            if field.required and field.default is None:
                if config:
                    if config.get(field_name, None) is None:
                        return True
                else:
                    return True
        return False

    _config_hash: str = PrivateAttr(default=None)
    constants: Dict[str, Any] = Field(
        default_factory=dict, description="Value constants for this module."
    )
    defaults: Dict[str, Any] = Field(
        default_factory=dict, description="Value defaults for this module."
    )

    class Config:
        extra = Extra.forbid
        validate_assignment = True

    def get(self, key: str) -> Any:
        """Get the value for the specified configuation key."""

        if key not in self.__fields__:
            raise Exception(
                f"No config value '{key}' in module config class '{self.__class__.__name__}'."
            )

        return getattr(self, key)

    def create_renderable(self, **config: Any) -> RenderableType:

        my_table = Table(box=box.MINIMAL, show_header=False)
        my_table.add_column("Field name", style="i")
        my_table.add_column("Value")
        for field in self.__fields__:
            attr = getattr(self, field)
            if isinstance(attr, str):
                attr_str = attr
            elif hasattr(attr, "create_renderable"):
                attr_str = attr.create_renderable()
            elif isinstance(attr, BaseModel):
                attr_str = attr.json(option=orjson.orjson.OPT_INDENT_2)
            else:
                attr_str = str(attr)
            my_table.add_row(field, attr_str)

        return my_table

Attributes

constants: Dict[str, Any] pydantic-field

Value constants for this module.

defaults: Dict[str, Any] pydantic-field

Value defaults for this module.

Config

Source code in kiara/models/module/__init__.py

class Config:
    extra = Extra.forbid
    validate_assignment = True

extra

validate_assignment

Methods

create_renderable(self, **config)

Source code in kiara/models/module/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:

    my_table = Table(box=box.MINIMAL, show_header=False)
    my_table.add_column("Field name", style="i")
    my_table.add_column("Value")
    for field in self.__fields__:
        attr = getattr(self, field)
        if isinstance(attr, str):
            attr_str = attr
        elif hasattr(attr, "create_renderable"):
            attr_str = attr.create_renderable()
        elif isinstance(attr, BaseModel):
            attr_str = attr.json(option=orjson.orjson.OPT_INDENT_2)
        else:
            attr_str = str(attr)
        my_table.add_row(field, attr_str)

    return my_table

get(self, key)

Get the value for the specified configuation key.

Source code in kiara/models/module/__init__.py

def get(self, key: str) -> Any:
    """Get the value for the specified configuation key."""

    if key not in self.__fields__:
        raise Exception(
            f"No config value '{key}' in module config class '{self.__class__.__name__}'."
        )

    return getattr(self, key)

requires_config(config=None) classmethod

Return whether this class can be used as-is, or requires configuration before an instance can be created.

Source code in kiara/models/module/__init__.py

@classmethod
def requires_config(cls, config: Optional[Mapping[str, Any]] = None) -> bool:
    """Return whether this class can be used as-is, or requires configuration before an instance can be created."""

    for field_name, field in cls.__fields__.items():
        if field.required and field.default is None:
            if config:
                if config.get(field_name, None) is None:
                    return True
            else:
                return True
    return False

KiaraModuleConfigMetadata (KiaraModel) pydantic-model

Source code in kiara/models/module/__init__.py

class KiaraModuleConfigMetadata(KiaraModel):

    _kiara_model_id = "metadata.module_config"

    @classmethod
    def from_config_class(
        cls,
        config_cls: Type[KiaraModuleConfig],
    ):

        flat_models = get_flat_models_from_model(config_cls)
        model_name_map = get_model_name_map(flat_models)
        m_schema, _, _ = model_process_schema(config_cls, model_name_map=model_name_map)
        fields = m_schema["properties"]

        config_values = {}
        for field_name, details in fields.items():

            type_str = "-- n/a --"
            if "type" in details.keys():
                type_str = details["type"]

            desc = details.get("description", DEFAULT_NO_DESC_VALUE)
            default = config_cls.__fields__[field_name].default
            if default is None:
                if callable(config_cls.__fields__[field_name].default_factory):
                    default = config_cls.__fields__[field_name].default_factory()  # type: ignore

            req = config_cls.__fields__[field_name].required

            config_values[field_name] = ValueTypeAndDescription(
                description=desc, type=type_str, value_default=default, required=req
            )

        python_cls = PythonClass.from_class(config_cls)
        return KiaraModuleConfigMetadata(
            python_class=python_cls, config_values=config_values
        )

    python_class: PythonClass = Field(description="The config model python class.")
    config_values: Dict[str, ValueTypeAndDescription] = Field(
        description="The available configuration values."
    )

    def _retrieve_id(self) -> str:
        return self.python_class.full_name

    def _retrieve_data_to_hash(self) -> Any:
        return self.python_class.full_name

Attributes

config_values: Dict[str, kiara.models.module.ValueTypeAndDescription] pydantic-field required

The available configuration values.

python_class: PythonClass pydantic-field required

The config model python class.

from_config_class(config_cls) classmethod

Source code in kiara/models/module/__init__.py

@classmethod
def from_config_class(
    cls,
    config_cls: Type[KiaraModuleConfig],
):

    flat_models = get_flat_models_from_model(config_cls)
    model_name_map = get_model_name_map(flat_models)
    m_schema, _, _ = model_process_schema(config_cls, model_name_map=model_name_map)
    fields = m_schema["properties"]

    config_values = {}
    for field_name, details in fields.items():

        type_str = "-- n/a --"
        if "type" in details.keys():
            type_str = details["type"]

        desc = details.get("description", DEFAULT_NO_DESC_VALUE)
        default = config_cls.__fields__[field_name].default
        if default is None:
            if callable(config_cls.__fields__[field_name].default_factory):
                default = config_cls.__fields__[field_name].default_factory()  # type: ignore

        req = config_cls.__fields__[field_name].required

        config_values[field_name] = ValueTypeAndDescription(
            description=desc, type=type_str, value_default=default, required=req
        )

    python_cls = PythonClass.from_class(config_cls)
    return KiaraModuleConfigMetadata(
        python_class=python_cls, config_values=config_values
    )

KiaraModuleTypeInfo (TypeInfo) pydantic-model

Source code in kiara/models/module/__init__.py

class KiaraModuleTypeInfo(TypeInfo["KiaraModule"]):

    _kiara_model_id = "info.kiara_module_type"

    @classmethod
    def create_from_type_class(
        cls, type_cls: Type["KiaraModule"]
    ) -> "KiaraModuleTypeInfo":

        module_attrs = cls.extract_module_attributes(module_cls=type_cls)
        return cls.construct(**module_attrs)

    @classmethod
    def base_class(self) -> Type["KiaraModule"]:

        from kiara.modules import KiaraModule

        return KiaraModule

    @classmethod
    def category_name(cls) -> str:
        return "module"

    @classmethod
    def extract_module_attributes(
        self, module_cls: Type["KiaraModule"]
    ) -> Dict[str, Any]:

        if not hasattr(module_cls, "process"):
            raise Exception(f"Module class '{module_cls}' misses 'process' method.")
        proc_src = textwrap.dedent(inspect.getsource(module_cls.process))  # type: ignore

        authors_md = AuthorsMetadataModel.from_class(module_cls)
        doc = DocumentationMetadataModel.from_class_doc(module_cls)
        python_class = PythonClass.from_class(module_cls)
        properties_md = ContextMetadataModel.from_class(module_cls)
        config = KiaraModuleConfigMetadata.from_config_class(module_cls._config_cls)

        return {
            "type_name": module_cls._module_type_name,  # type: ignore
            "documentation": doc,
            "authors": authors_md,
            "context": properties_md,
            "python_class": python_class,
            "config": config,
            "process_src": proc_src,
        }

    process_src: str = Field(
        description="The source code of the process method of the module."
    )

    def create_renderable(self, **config: Any) -> RenderableType:

        include_config_schema = config.get("include_config_schema", True)
        include_src = config.get("include_src", True)
        include_doc = config.get("include_doc", True)

        table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
        table.add_column("property", style="i")
        table.add_column("value")

        if include_doc:
            table.add_row(
                "Documentation",
                Panel(self.documentation.create_renderable(), box=box.SIMPLE),
            )
        table.add_row("Author(s)", self.authors.create_renderable())
        table.add_row("Context", self.context.create_renderable())

        if include_config_schema:
            config_cls = self.python_class.get_class()._config_cls  # type: ignore
            from kiara.utils.output import create_table_from_base_model_cls

            table.add_row(
                "Module config schema", create_table_from_base_model_cls(config_cls)
            )

        table.add_row("Python class", self.python_class.create_renderable())

        if include_src:
            _config = Syntax(self.process_src, "python", background_color="default")
            table.add_row("Processing source code", Panel(_config, box=box.HORIZONTALS))

        return table

Attributes

process_src: str pydantic-field required

The source code of the process method of the module.

base_class() classmethod

Source code in kiara/models/module/__init__.py

@classmethod
def base_class(self) -> Type["KiaraModule"]:

    from kiara.modules import KiaraModule

    return KiaraModule

category_name() classmethod

Source code in kiara/models/module/__init__.py

@classmethod
def category_name(cls) -> str:
    return "module"

create_from_type_class(type_cls) classmethod

Source code in kiara/models/module/__init__.py

@classmethod
def create_from_type_class(
    cls, type_cls: Type["KiaraModule"]
) -> "KiaraModuleTypeInfo":

    module_attrs = cls.extract_module_attributes(module_cls=type_cls)
    return cls.construct(**module_attrs)

create_renderable(self, **config)

Source code in kiara/models/module/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:

    include_config_schema = config.get("include_config_schema", True)
    include_src = config.get("include_src", True)
    include_doc = config.get("include_doc", True)

    table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
    table.add_column("property", style="i")
    table.add_column("value")

    if include_doc:
        table.add_row(
            "Documentation",
            Panel(self.documentation.create_renderable(), box=box.SIMPLE),
        )
    table.add_row("Author(s)", self.authors.create_renderable())
    table.add_row("Context", self.context.create_renderable())

    if include_config_schema:
        config_cls = self.python_class.get_class()._config_cls  # type: ignore
        from kiara.utils.output import create_table_from_base_model_cls

        table.add_row(
            "Module config schema", create_table_from_base_model_cls(config_cls)
        )

    table.add_row("Python class", self.python_class.create_renderable())

    if include_src:
        _config = Syntax(self.process_src, "python", background_color="default")
        table.add_row("Processing source code", Panel(_config, box=box.HORIZONTALS))

    return table

extract_module_attributes(module_cls) classmethod

Source code in kiara/models/module/__init__.py

@classmethod
def extract_module_attributes(
    self, module_cls: Type["KiaraModule"]
) -> Dict[str, Any]:

    if not hasattr(module_cls, "process"):
        raise Exception(f"Module class '{module_cls}' misses 'process' method.")
    proc_src = textwrap.dedent(inspect.getsource(module_cls.process))  # type: ignore

    authors_md = AuthorsMetadataModel.from_class(module_cls)
    doc = DocumentationMetadataModel.from_class_doc(module_cls)
    python_class = PythonClass.from_class(module_cls)
    properties_md = ContextMetadataModel.from_class(module_cls)
    config = KiaraModuleConfigMetadata.from_config_class(module_cls._config_cls)

    return {
        "type_name": module_cls._module_type_name,  # type: ignore
        "documentation": doc,
        "authors": authors_md,
        "context": properties_md,
        "python_class": python_class,
        "config": config,
        "process_src": proc_src,
    }

ModuleTypeClassesInfo (TypeInfoModelGroup) pydantic-model

Source code in kiara/models/module/__init__.py

class ModuleTypeClassesInfo(TypeInfoModelGroup):

    _kiara_model_id = "info.module_types"

    @classmethod
    def base_info_class(cls) -> Type[TypeInfo]:
        return KiaraModuleTypeInfo

    type_name: Literal["module_type"] = "module_type"
    type_infos: Mapping[str, KiaraModuleTypeInfo] = Field(
        description="The module type info instances for each type."
    )

Attributes

type_infos: Mapping[str, kiara.models.module.KiaraModuleTypeInfo] pydantic-field required

The module type info instances for each type.

type_name: Literal['module_type'] pydantic-field

base_info_class() classmethod

Source code in kiara/models/module/__init__.py

@classmethod
def base_info_class(cls) -> Type[TypeInfo]:
    return KiaraModuleTypeInfo

ValueTypeAndDescription (BaseModel) pydantic-model

Source code in kiara/models/module/__init__.py

class ValueTypeAndDescription(BaseModel):

    description: str = Field(description="The description for the value.")
    type: str = Field(description="The value type.")
    value_default: Any = Field(description="Default for the value.", default=None)
    required: bool = Field(description="Whether this value is required")

Attributes

description: str pydantic-field required

The description for the value.

required: bool pydantic-field required

Whether this value is required

type: str pydantic-field required

The value type.

value_default: Any pydantic-field

Default for the value.

calculate_class_doc_url(base_url, module_type_name)

Source code in kiara/models/module/__init__.py

def calculate_class_doc_url(base_url: str, module_type_name: str):

    if base_url.endswith("/"):
        base_url = base_url[0:-1]

    module_type_name = module_type_name.replace(".", "")
    url = f"{base_url}/latest/modules_list/#{module_type_name}"

    return url

calculate_class_source_url(base_url, python_class_info, branch='main')

Source code in kiara/models/module/__init__.py

def calculate_class_source_url(
    base_url: str, python_class_info: PythonClass, branch: str = "main"
):

    if base_url.endswith("/"):
        base_url = base_url[0:-1]

    m = python_class_info.get_python_module()
    m_file = m.__file__
    assert m_file is not None

    base_url = f"{base_url}/blob/{branch}/src/{python_class_info.python_module_name.replace('.', '/')}"

    if m_file.endswith("__init__.py"):
        url = f"{base_url}/__init__.py"
    else:
        url = f"{base_url}.py"

    return url

Modules

destiniy

Classes

Destiny (Manifest) pydantic-model

A destiny is basically a link to a potential future transformation result involving one or several values as input.

It is immutable, once executed, each of the input values can only have one destiny with a specific alias. This is similar to what is usually called a 'future' in programming languages, but more deterministic, sorta.

Source code in kiara/models/module/destiniy.py

class Destiny(Manifest):
    """A destiny is basically a link to a potential future transformation result involving one or several values as input.

    It is immutable, once executed, each of the input values can only have one destiny with a specific alias.
    This is similar to what is usually called a 'future' in programming languages, but more deterministic, sorta.
    """

    _kiara_model_id = "instance.destiny"

    @classmethod
    def create_from_values(
        cls,
        kiara: "Kiara",
        destiny_alias: str,
        values: Mapping[str, uuid.UUID],
        manifest: Manifest,
        result_field_name: Optional[str] = None,
    ):

        module = kiara.create_module(manifest=manifest)

        if result_field_name is None:
            if len(module.outputs_schema) != 1:
                raise Exception(
                    f"Can't determine result field name for module, not provided, and multiple outputs available for module '{module.module_type_name}': {', '.join(module.outputs_schema.keys())}."
                )

            result_field_name = next(iter(module.outputs_schema.keys()))

        result_schema = module.outputs_schema.get(result_field_name, None)
        if result_schema is None:
            raise Exception(
                f"Can't determine result schema for module '{module.module_type_name}', result field '{result_field_name}' not available. Available field: {', '.join(module.outputs_schema.keys())}"
            )

        fixed_inputs = {}
        deferred_inputs: Dict[str, None] = {}
        for field in module.inputs_schema.keys():
            if field in values.keys():
                fixed_inputs[field] = values[field]
            else:
                deferred_inputs[field] = None

        module_details = KiaraModuleClass.from_module(module=module)

        # TODO: check whether it'd be better to 'resolve' the module config, as this might change the resulting hash
        destiny_id: uuid.UUID = ID_REGISTRY.generate(obj_type=Destiny)
        destiny = Destiny(
            destiny_id=destiny_id,
            destiny_alias=destiny_alias,
            module_details=module_details,
            module_type=manifest.module_type,
            module_config=manifest.module_config,
            result_field_name=result_field_name,
            result_schema=result_schema,
            fixed_inputs=fixed_inputs,
            inputs_schema=dict(module.inputs_schema),
            deferred_inputs=deferred_inputs,
        )
        destiny._module = module
        ID_REGISTRY.update_metadata(destiny_id, obj=destiny)
        return destiny

    destiny_id: uuid.UUID = Field(description="The id of this destiny.")

    destiny_alias: str = Field(description="The path to (the) destiny.")
    module_details: KiaraModuleClass = Field(
        description="The class of the underlying module."
    )
    fixed_inputs: Dict[str, uuid.UUID] = Field(
        description="Inputs that are known in advance."
    )
    inputs_schema: Dict[str, ValueSchema] = Field(
        description="The schemas of all deferred input fields."
    )
    deferred_inputs: Dict[str, Optional[uuid.UUID]] = Field(
        description="Potentially required external inputs that are needed for this destiny to materialize."
    )
    result_field_name: str = Field(description="The name of the result field.")
    result_schema: ValueSchema = Field(description="The value schema of the result.")
    result_value_id: Optional[uuid.UUID] = Field(
        description="The value id of the result."
    )

    _is_stored: bool = PrivateAttr(default=False)
    _job_id: Optional[uuid.UUID] = PrivateAttr(default=None)

    _merged_inputs: Optional[Dict[str, uuid.UUID]] = PrivateAttr(default=None)
    # _job_config_hash: Optional[int] = PrivateAttr(default=None)
    _module: Optional["KiaraModule"] = PrivateAttr(default=None)

    def _retrieve_id(self) -> str:
        return str(self.destiny_id)

    def _retrieve_data_to_hash(self) -> Any:
        return self.destiny_id.bytes

    # @property
    # def job_config_hash(self) -> int:
    #     if self._job_config_hash is None:
    #         self._job_config_hash = self._retrieve_job_config_hash()
    #     return self._job_config_hash

    @property
    def merged_inputs(self) -> Mapping[str, uuid.UUID]:

        if self._merged_inputs is not None:
            return self._merged_inputs

        result = copy.copy(self.fixed_inputs)
        missing = []
        for k in self.inputs_schema.keys():
            if k in self.fixed_inputs.keys():
                if k in self.deferred_inputs.keys():
                    raise Exception(
                        f"Destiny input field '{k}' present in both fixed and deferred inputs, this is invalid."
                    )
                else:
                    continue
            v = self.deferred_inputs.get(k, None)
            if v is None or isinstance(v, SpecialValue):
                missing.append(k)
            else:
                result[k] = v

        if missing:
            raise Exception(
                f"Destiny not valid (yet), missing inputs: {', '.join(missing)}"
            )

        self._merged_inputs = result
        return self._merged_inputs

    @property
    def module(self) -> "KiaraModule":
        if self._module is None:
            m_cls = self.module_details.get_class()
            self._module = m_cls(module_config=self.module_config)
        return self._module

    # def _retrieve_job_config_hash(self) -> int:
    #     obj = {"module_config": self.manifest_data, "inputs": self.merged_inputs}
    #     return compute_cid(obj)

Attributes

deferred_inputs: Dict[str, Optional[uuid.UUID]] pydantic-field required

Potentially required external inputs that are needed for this destiny to materialize.

destiny_alias: str pydantic-field required

The path to (the) destiny.

destiny_id: UUID pydantic-field required

The id of this destiny.

fixed_inputs: Dict[str, uuid.UUID] pydantic-field required

Inputs that are known in advance.

inputs_schema: Dict[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

The schemas of all deferred input fields.

merged_inputs: Mapping[str, uuid.UUID] property readonly

module: KiaraModule property readonly

module_details: KiaraModuleClass pydantic-field required

The class of the underlying module.

result_field_name: str pydantic-field required

The name of the result field.

result_schema: ValueSchema pydantic-field required

The value schema of the result.

result_value_id: UUID pydantic-field

The value id of the result.

create_from_values(kiara, destiny_alias, values, manifest, result_field_name=None) classmethod

Source code in kiara/models/module/destiniy.py

@classmethod
def create_from_values(
    cls,
    kiara: "Kiara",
    destiny_alias: str,
    values: Mapping[str, uuid.UUID],
    manifest: Manifest,
    result_field_name: Optional[str] = None,
):

    module = kiara.create_module(manifest=manifest)

    if result_field_name is None:
        if len(module.outputs_schema) != 1:
            raise Exception(
                f"Can't determine result field name for module, not provided, and multiple outputs available for module '{module.module_type_name}': {', '.join(module.outputs_schema.keys())}."
            )

        result_field_name = next(iter(module.outputs_schema.keys()))

    result_schema = module.outputs_schema.get(result_field_name, None)
    if result_schema is None:
        raise Exception(
            f"Can't determine result schema for module '{module.module_type_name}', result field '{result_field_name}' not available. Available field: {', '.join(module.outputs_schema.keys())}"
        )

    fixed_inputs = {}
    deferred_inputs: Dict[str, None] = {}
    for field in module.inputs_schema.keys():
        if field in values.keys():
            fixed_inputs[field] = values[field]
        else:
            deferred_inputs[field] = None

    module_details = KiaraModuleClass.from_module(module=module)

    # TODO: check whether it'd be better to 'resolve' the module config, as this might change the resulting hash
    destiny_id: uuid.UUID = ID_REGISTRY.generate(obj_type=Destiny)
    destiny = Destiny(
        destiny_id=destiny_id,
        destiny_alias=destiny_alias,
        module_details=module_details,
        module_type=manifest.module_type,
        module_config=manifest.module_config,
        result_field_name=result_field_name,
        result_schema=result_schema,
        fixed_inputs=fixed_inputs,
        inputs_schema=dict(module.inputs_schema),
        deferred_inputs=deferred_inputs,
    )
    destiny._module = module
    ID_REGISTRY.update_metadata(destiny_id, obj=destiny)
    return destiny

jobs

Classes

ActiveJob (KiaraModel) pydantic-model

Source code in kiara/models/module/jobs.py

class ActiveJob(KiaraModel):

    _kiara_model_id = "instance.active_job"

    job_id: uuid.UUID = Field(description="The job id.")

    job_config: JobConfig = Field(description="The job details.")
    status: JobStatus = Field(
        description="The current status of the job.", default=JobStatus.CREATED
    )
    job_log: JobLog = Field(description="The lob jog.")
    submitted: datetime = Field(
        description="When the job was submitted.", default_factory=datetime.now
    )
    started: Optional[datetime] = Field(
        description="When the job was started.", default=None
    )
    finished: Optional[datetime] = Field(
        description="When the job was finished.", default=None
    )
    results: Optional[Dict[str, uuid.UUID]] = Field(description="The result(s).")
    error: Optional[str] = Field(description="Potential error message.")
    _exception: Optional[Exception] = PrivateAttr(default=None)

    def _retrieve_id(self) -> str:
        return str(self.job_id)

    def _retrieve_data_to_hash(self) -> Any:
        return self.job_id.bytes

    @property
    def exception(self) -> Optional[Exception]:
        return self._exception

    @property
    def runtime(self) -> Optional[float]:

        if self.started is None or self.finished is None:
            return None

        runtime = self.finished - self.started
        return runtime.total_seconds()

Attributes

error: str pydantic-field

Potential error message.

exception: Optional[Exception] property readonly

finished: datetime pydantic-field

When the job was finished.

job_config: JobConfig pydantic-field required

The job details.

job_id: UUID pydantic-field required

The job id.

job_log: JobLog pydantic-field required

The lob jog.

results: Dict[str, uuid.UUID] pydantic-field

The result(s).

runtime: Optional[float] property readonly

started: datetime pydantic-field

When the job was started.

status: JobStatus pydantic-field

The current status of the job.

submitted: datetime pydantic-field

When the job was submitted.

JobConfig (InputsManifest) pydantic-model

Source code in kiara/models/module/jobs.py

class JobConfig(InputsManifest):

    _kiara_model_id = "instance.job_config"

    @classmethod
    def create_from_module(
        cls,
        data_registry: "DataRegistry",
        module: "KiaraModule",
        inputs: Mapping[str, Any],
    ):

        augmented = module.augment_module_inputs(inputs=inputs)
        values = data_registry.create_valueset(
            data=augmented, schema=module.inputs_schema
        )
        invalid = values.check_invalid()
        if invalid:
            raise InvalidValuesException(invalid_values=invalid)

        value_ids = values.get_all_value_ids()
        return JobConfig.construct(
            module_type=module.module_type_name,
            module_config=module.config.dict(),
            inputs=value_ids,
        )

    def _retrieve_data_to_hash(self) -> Any:
        return {"manifest": self.manifest_cid, "inputs": self.inputs_cid}

create_from_module(data_registry, module, inputs) classmethod

Source code in kiara/models/module/jobs.py

@classmethod
def create_from_module(
    cls,
    data_registry: "DataRegistry",
    module: "KiaraModule",
    inputs: Mapping[str, Any],
):

    augmented = module.augment_module_inputs(inputs=inputs)
    values = data_registry.create_valueset(
        data=augmented, schema=module.inputs_schema
    )
    invalid = values.check_invalid()
    if invalid:
        raise InvalidValuesException(invalid_values=invalid)

    value_ids = values.get_all_value_ids()
    return JobConfig.construct(
        module_type=module.module_type_name,
        module_config=module.config.dict(),
        inputs=value_ids,
    )

JobLog (BaseModel) pydantic-model

Source code in kiara/models/module/jobs.py

class JobLog(BaseModel):

    log: List[LogMessage] = Field(
        description="The logs for this job.", default_factory=list
    )
    percent_finished: int = Field(
        description="Describes how much of the job is finished. A negative number means the module does not support progress tracking.",
        default=-1,
    )

    def add_log(self, msg: str, log_level: int = logging.DEBUG):

        _msg = LogMessage(msg=msg, log_level=log_level)
        self.log.append(_msg)

Attributes

log: List[kiara.models.module.jobs.LogMessage] pydantic-field

The logs for this job.

percent_finished: int pydantic-field

Describes how much of the job is finished. A negative number means the module does not support progress tracking.

add_log(self, msg, log_level=10)

Source code in kiara/models/module/jobs.py

def add_log(self, msg: str, log_level: int = logging.DEBUG):

    _msg = LogMessage(msg=msg, log_level=log_level)
    self.log.append(_msg)

JobRecord (JobConfig) pydantic-model

Source code in kiara/models/module/jobs.py

class JobRecord(JobConfig):

    _kiara_model_id = "instance.job_record"

    @classmethod
    def from_active_job(self, active_job: ActiveJob):

        assert active_job.status == JobStatus.SUCCESS
        assert active_job.results is not None

        job_details = JobRuntimeDetails.construct(
            job_log=active_job.job_log,
            submitted=active_job.submitted,
            started=active_job.started,  # type: ignore
            finished=active_job.finished,  # type: ignore
            runtime=active_job.runtime,  # type: ignore
        )

        job_record = JobRecord.construct(
            job_id=active_job.job_id,
            module_type=active_job.job_config.module_type,
            module_config=active_job.job_config.module_config,
            inputs=active_job.job_config.inputs,
            outputs=active_job.results,
            runtime_details=job_details,
        )
        return job_record

    job_id: uuid.UUID = Field(description="The globally unique id for this job.")
    outputs: Dict[str, uuid.UUID] = Field(description="References to the job outputs.")
    runtime_details: Optional[JobRuntimeDetails] = Field(
        description="Runtime details for the job."
    )

    _is_stored: bool = PrivateAttr(default=None)
    _outputs_hash: Optional[int] = PrivateAttr(default=None)

    def _retrieve_data_to_hash(self) -> Any:
        return {
            "manifest": self.manifest_cid,
            "inputs": self.inputs_cid,
            "outputs": {k: v.bytes for k, v in self.outputs.items()},
        }

    @property
    def outputs_hash(self) -> int:

        if self._outputs_hash is not None:
            return self._outputs_hash

        obj = self.outputs
        h = DeepHash(obj, hasher=KIARA_HASH_FUNCTION)
        self._outputs_hash = h[obj]
        return self._outputs_hash

Attributes

job_id: UUID pydantic-field required

The globally unique id for this job.

outputs: Dict[str, uuid.UUID] pydantic-field required

References to the job outputs.

outputs_hash: int property readonly

runtime_details: JobRuntimeDetails pydantic-field

Runtime details for the job.

from_active_job(active_job) classmethod

Source code in kiara/models/module/jobs.py

@classmethod
def from_active_job(self, active_job: ActiveJob):

    assert active_job.status == JobStatus.SUCCESS
    assert active_job.results is not None

    job_details = JobRuntimeDetails.construct(
        job_log=active_job.job_log,
        submitted=active_job.submitted,
        started=active_job.started,  # type: ignore
        finished=active_job.finished,  # type: ignore
        runtime=active_job.runtime,  # type: ignore
    )

    job_record = JobRecord.construct(
        job_id=active_job.job_id,
        module_type=active_job.job_config.module_type,
        module_config=active_job.job_config.module_config,
        inputs=active_job.job_config.inputs,
        outputs=active_job.results,
        runtime_details=job_details,
    )
    return job_record

JobRuntimeDetails (BaseModel) pydantic-model

Source code in kiara/models/module/jobs.py

class JobRuntimeDetails(BaseModel):

    # @classmethod
    # def from_manifest(
    #     cls,
    #     manifest: Manifest,
    #     inputs: Mapping[str, Value],
    #     outputs: Mapping[str, Value],
    # ):
    #
    #     return JobRecord(
    #         module_type=manifest.module_type,
    #         module_config=manifest.module_config,
    #         inputs={k: v.value_id for k, v in inputs.items()},
    #         outputs={k: v.value_id for k, v in outputs.items()},
    #     )

    job_log: JobLog = Field(description="The lob jog.")
    submitted: datetime = Field(description="When the job was submitted.")
    started: datetime = Field(description="When the job was started.")
    finished: datetime = Field(description="When the job was finished.")
    runtime: float = Field(description="The duration of the job.")

Attributes

finished: datetime pydantic-field required

When the job was finished.

job_log: JobLog pydantic-field required

The lob jog.

runtime: float pydantic-field required

The duration of the job.

started: datetime pydantic-field required

When the job was started.

submitted: datetime pydantic-field required

When the job was submitted.

JobStatus (Enum)

An enumeration.

Source code in kiara/models/module/jobs.py

class JobStatus(Enum):

    CREATED = "__job_created__"
    STARTED = "__job_started__"
    SUCCESS = "__job_success__"
    FAILED = "__job_failed__"

CREATED

FAILED

STARTED

SUCCESS

LogMessage (BaseModel) pydantic-model

Source code in kiara/models/module/jobs.py

class LogMessage(BaseModel):

    timestamp: datetime = Field(
        description="The time the message was logged.", default_factory=datetime.now
    )
    log_level: int = Field(description="The log level.")
    msg: str = Field(description="The log message")

Attributes

log_level: int pydantic-field required

The log level.

msg: str pydantic-field required

The log message

timestamp: datetime pydantic-field

The time the message was logged.

manifest

Classes

InputsManifest (Manifest) pydantic-model

Source code in kiara/models/module/manifest.py

class InputsManifest(Manifest):

    _kiara_model_id = "instance.manifest_with_inputs"

    inputs: Mapping[str, uuid.UUID] = Field(
        description="A map of all the input fields and value references."
    )
    _inputs_cid: Optional[CID] = PrivateAttr(default=None)
    _jobs_cid: Optional[CID] = PrivateAttr(default=None)

    @validator("inputs")
    def replace_none_values(cls, value):
        result = {}
        for k, v in value.items():
            if v is None:
                v = NONE_VALUE_ID
            result[k] = v
        return result

    @property
    def job_hash(self) -> str:

        return str(self.job_cid)

    @property
    def job_cid(self) -> CID:

        if self._jobs_cid is not None:
            return self._jobs_cid

        obj = {"manifest": self.manifest_cid, "inputs": self.inputs_cid}
        _, self._jobs_cid = compute_cid(data=obj)
        return self._jobs_cid

    @property
    def inputs_cid(self) -> CID:
        if self._inputs_cid is not None:
            return self._inputs_cid

        _, cid = compute_cid(data={k: v.bytes for k, v in self.inputs.items()})
        self._inputs_cid = cid
        return self._inputs_cid

Attributes

inputs: Mapping[str, uuid.UUID] pydantic-field required

A map of all the input fields and value references.

inputs_cid: CID property readonly

job_cid: CID property readonly

job_hash: str property readonly

replace_none_values(value) classmethod

Source code in kiara/models/module/manifest.py

@validator("inputs")
def replace_none_values(cls, value):
    result = {}
    for k, v in value.items():
        if v is None:
            v = NONE_VALUE_ID
        result[k] = v
    return result

Manifest (KiaraModel) pydantic-model

A class to hold the type and configuration for a module instance.

Source code in kiara/models/module/manifest.py

class Manifest(KiaraModel):
    """A class to hold the type and configuration for a module instance."""

    _kiara_model_id = "instance.manifest"

    class Config:
        extra = Extra.forbid
        validate_all = True

    _manifest_data: Optional[Mapping[str, Any]] = PrivateAttr(default=None)
    _manifest_cid: Optional[CID] = PrivateAttr(default=None)

    module_type: str = Field(description="The module type.")
    module_config: Mapping[str, Any] = Field(
        default_factory=dict, description="The configuration for the module."
    )
    # python_class: PythonClass = Field(description="The python class that implements this module.")
    # doc: DocumentationMetadataModel = Field(
    #     description="Documentation for this module instance.", default=None
    # )

    # @validator("module_config")
    # def _validate_module_config(cls, value):
    #
    #     return value

    @property
    def manifest_data(self):
        """The configuration data for this module instance."""
        if self._manifest_data is not None:
            return self._manifest_data

        self._manifest_data = {
            "module_type": self.module_type,
            "module_config": self.module_config,
        }
        return self._manifest_data

    @property
    def manifest_cid(self) -> CID:
        if self._manifest_cid is not None:
            return self._manifest_cid

        _, self._manifest_cid = compute_cid(self.manifest_data)
        return self._manifest_cid

    def manifest_data_as_json(self):

        return self.json(include={"module_type", "module_config"})

    def _retrieve_data_to_hash(self) -> Any:
        return self.manifest_data

    def create_renderable(self, **config: Any) -> RenderableType:
        """Create a renderable for this module configuration."""

        data = self.dict(exclude_none=True)
        conf = Syntax(
            orjson_dumps(data, option=orjson.OPT_INDENT_2),
            "json",
            background_color="default",
        )
        return conf

    def __repr__(self):

        return f"{self.__class__.__name__}(module_type={self.module_type}, module_config={self.module_config})"

    def __str__(self):

        return self.__repr__()

Attributes

manifest_cid: CID property readonly

manifest_data property readonly

The configuration data for this module instance.

module_config: Mapping[str, Any] pydantic-field

The configuration for the module.

module_type: str pydantic-field required

The module type.

Config

Source code in kiara/models/module/manifest.py

class Config:
    extra = Extra.forbid
    validate_all = True

extra

validate_all

Methods

create_renderable(self, **config)

Create a renderable for this module configuration.

Source code in kiara/models/module/manifest.py

def create_renderable(self, **config: Any) -> RenderableType:
    """Create a renderable for this module configuration."""

    data = self.dict(exclude_none=True)
    conf = Syntax(
        orjson_dumps(data, option=orjson.OPT_INDENT_2),
        "json",
        background_color="default",
    )
    return conf

manifest_data_as_json(self)

Source code in kiara/models/module/manifest.py

def manifest_data_as_json(self):

    return self.json(include={"module_type", "module_config"})

operation

logger

Classes

BaseOperationDetails (OperationDetails) pydantic-model

Source code in kiara/models/module/operation.py

class BaseOperationDetails(OperationDetails):

    _kiara_model_id = "instance.operation_details.base"

    _op_schema: OperationSchema = PrivateAttr(default=None)

    def retrieve_inputs_schema(cls) -> ValueSetSchema:
        raise NotImplementedError()

    def retrieve_outputs_schema(cls) -> ValueSetSchema:
        raise NotImplementedError()

    def get_operation_schema(self) -> OperationSchema:

        if self._op_schema is not None:
            return self._op_schema

        self._op_schema = OperationSchema(
            alias=self.__class__.__name__,
            inputs_schema=self.retrieve_inputs_schema(),
            outputs_schema=self.retrieve_outputs_schema(),
        )
        return self._op_schema

get_operation_schema(self)

Source code in kiara/models/module/operation.py

def get_operation_schema(self) -> OperationSchema:

    if self._op_schema is not None:
        return self._op_schema

    self._op_schema = OperationSchema(
        alias=self.__class__.__name__,
        inputs_schema=self.retrieve_inputs_schema(),
        outputs_schema=self.retrieve_outputs_schema(),
    )
    return self._op_schema

retrieve_inputs_schema(cls)

Source code in kiara/models/module/operation.py

def retrieve_inputs_schema(cls) -> ValueSetSchema:
    raise NotImplementedError()

retrieve_outputs_schema(cls)

Source code in kiara/models/module/operation.py

def retrieve_outputs_schema(cls) -> ValueSetSchema:
    raise NotImplementedError()

ManifestOperationConfig (OperationConfig) pydantic-model

Source code in kiara/models/module/operation.py

class ManifestOperationConfig(OperationConfig):

    _kiara_model_id = "instance.operation_config.manifest"

    module_type: str = Field(description="The module type.")
    module_config: Dict[str, Any] = Field(
        default_factory=dict, description="The configuration for the module."
    )

    def retrieve_module_type(self, kiara: "Kiara") -> str:
        return self.module_type

    def retrieve_module_config(self, kiara: "Kiara") -> Mapping[str, Any]:
        return self.module_config

Attributes

module_config: Dict[str, Any] pydantic-field

The configuration for the module.

module_type: str pydantic-field required

The module type.

retrieve_module_config(self, kiara)

Source code in kiara/models/module/operation.py

def retrieve_module_config(self, kiara: "Kiara") -> Mapping[str, Any]:
    return self.module_config

retrieve_module_type(self, kiara)

Source code in kiara/models/module/operation.py

def retrieve_module_type(self, kiara: "Kiara") -> str:
    return self.module_type

Operation (Manifest) pydantic-model

Source code in kiara/models/module/operation.py

class Operation(Manifest):

    _kiara_model_id = "instance.operation"

    @classmethod
    def create_from_module(cls, module: KiaraModule, doc: Any = None) -> "Operation":

        from kiara.operations.included_core_operations import (
            CustomModuleOperationDetails,
        )

        op_id = f"{module.module_type_name}._{module.module_instance_cid}"

        details = CustomModuleOperationDetails.create_from_module(module=module)

        if doc is not None:
            doc = DocumentationMetadataModel.create(doc)
        else:
            doc = DocumentationMetadataModel.from_class_doc(module.__class__)

        operation = Operation(
            module_type=module.module_type_name,
            module_config=module.config.dict(),
            operation_id=op_id,
            operation_details=details,
            module_details=KiaraModuleClass.from_module(module),
            doc=doc,
        )
        operation._module = module
        return operation

    operation_id: str = Field(description="The (unique) id of this operation.")
    operation_details: OperationDetails = Field(
        description="The operation specific details of this operation."
    )
    doc: DocumentationMetadataModel = Field(
        description="Documentation for this operation."
    )

    module_details: KiaraModuleClass = Field(
        description="The class of the underlying module."
    )
    metadata: Mapping[str, Any] = Field(
        description="Additional metadata for this operation.", default_factory=dict
    )

    _module: Optional["KiaraModule"] = PrivateAttr(default=None)

    def _retrieve_data_to_hash(self) -> Any:
        return {"operation_id": self.operation_id, "manifest": self.manifest_cid}

    @property
    def module(self) -> "KiaraModule":
        if self._module is None:
            m_cls = self.module_details.get_class()
            self._module = m_cls(module_config=self.module_config)
        return self._module

    @property
    def inputs_schema(self) -> Mapping[str, ValueSchema]:
        return self.operation_details.inputs_schema

    @property
    def outputs_schema(self) -> Mapping[str, ValueSchema]:
        return self.operation_details.outputs_schema

    def prepare_job_config(
        self, kiara: "Kiara", inputs: Mapping[str, Any]
    ) -> JobConfig:

        augmented_inputs = (
            self.operation_details.get_operation_schema().augment_module_inputs(
                inputs=inputs
            )
        )

        module_inputs = self.operation_details.create_module_inputs(
            inputs=augmented_inputs
        )

        job_config = kiara.job_registry.prepare_job_config(
            manifest=self, inputs=module_inputs
        )
        return job_config

    def run(self, kiara: "Kiara", inputs: Any) -> ValueMap:

        logger.debug("run.operation", operation_id=self.operation_id)
        job_config = self.prepare_job_config(kiara=kiara, inputs=inputs)

        job_id = kiara.job_registry.execute_job(job_config=job_config)
        outputs: ValueMap = kiara.job_registry.retrieve_result(job_id=job_id)

        result = self.process_job_outputs(outputs=outputs)

        return result

    def process_job_outputs(self, outputs: ValueMap) -> ValueMap:

        op_outputs = self.operation_details.create_operation_outputs(outputs=outputs)

        value_set = ValueMapReadOnly(value_items=op_outputs, values_schema=self.outputs_schema)  # type: ignore
        return value_set

    # def run(self, _attach_lineage: bool = True, **inputs: Any) -> ValueMap:
    #
    #     return self.module.run(_attach_lineage=_attach_lineage, **inputs)

    def create_renderable(self, **config: Any) -> RenderableType:
        """Create a printable overview of this operations details.

        Available render_config options:
          - 'include_full_doc' (default: True): whether to include the full documentation, or just a description
          - 'include_src' (default: False): whether to include the module source code
        """

        include_full_doc = config.get("include_full_doc", True)
        include_src = config.get("include_src", False)
        include_inputs = config.get("include_inputs", True)
        include_outputs = config.get("include_outputs", True)
        include_module_details = config.get("include_module_details", False)

        table = Table(box=box.SIMPLE, show_header=False, show_lines=True)
        table.add_column("Property", style="i")
        table.add_column("Value")

        if self.doc:
            if include_full_doc:
                table.add_row("Documentation", self.doc.full_doc)
            else:
                table.add_row("Description", self.doc.description)

        # module_type_md = self.module.get_type_metadata()

        if include_inputs:
            inputs_table = create_table_from_field_schemas(
                _add_required=True,
                _add_default=True,
                _show_header=True,
                _constants=None,
                **self.operation_details.inputs_schema,
            )
            table.add_row("Inputs", inputs_table)
        if include_outputs:
            outputs_table = create_table_from_field_schemas(
                _add_required=False,
                _add_default=False,
                _show_header=True,
                _constants=None,
                **self.operation_details.outputs_schema,
            )
            table.add_row("Outputs", outputs_table)

        if include_module_details:
            table.add_row("Module type", self.module_type)

            module_config = self.module.config.json(option=orjson.OPT_INDENT_2)
            conf = Syntax(
                module_config,
                "json",
                background_color="default",
            )
            table.add_row("Module config", conf)

            module_type_md = KiaraModuleTypeInfo.create_from_type_class(
                self.module_details.get_class()  # type: ignore
            )

            desc = module_type_md.documentation.description
            module_md = module_type_md.create_renderable(
                include_doc=False, include_src=False, include_config_schema=False
            )
            m_md = Group(desc, module_md)
            table.add_row("Module metadata", m_md)

        if include_src:
            table.add_row("Source code", module_type_md.process_src)

        return table

Attributes

doc: DocumentationMetadataModel pydantic-field required

Documentation for this operation.

inputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

metadata: Mapping[str, Any] pydantic-field

Additional metadata for this operation.

module: KiaraModule property readonly

module_details: KiaraModuleClass pydantic-field required

The class of the underlying module.

operation_details: OperationDetails pydantic-field required

The operation specific details of this operation.

operation_id: str pydantic-field required

The (unique) id of this operation.

outputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

Methods

create_from_module(module, doc=None) classmethod

Source code in kiara/models/module/operation.py

@classmethod
def create_from_module(cls, module: KiaraModule, doc: Any = None) -> "Operation":

    from kiara.operations.included_core_operations import (
        CustomModuleOperationDetails,
    )

    op_id = f"{module.module_type_name}._{module.module_instance_cid}"

    details = CustomModuleOperationDetails.create_from_module(module=module)

    if doc is not None:
        doc = DocumentationMetadataModel.create(doc)
    else:
        doc = DocumentationMetadataModel.from_class_doc(module.__class__)

    operation = Operation(
        module_type=module.module_type_name,
        module_config=module.config.dict(),
        operation_id=op_id,
        operation_details=details,
        module_details=KiaraModuleClass.from_module(module),
        doc=doc,
    )
    operation._module = module
    return operation

create_renderable(self, **config)

Create a printable overview of this operations details.

Available render_config options: - 'include_full_doc' (default: True): whether to include the full documentation, or just a description - 'include_src' (default: False): whether to include the module source code

Source code in kiara/models/module/operation.py

def create_renderable(self, **config: Any) -> RenderableType:
    """Create a printable overview of this operations details.

    Available render_config options:
      - 'include_full_doc' (default: True): whether to include the full documentation, or just a description
      - 'include_src' (default: False): whether to include the module source code
    """

    include_full_doc = config.get("include_full_doc", True)
    include_src = config.get("include_src", False)
    include_inputs = config.get("include_inputs", True)
    include_outputs = config.get("include_outputs", True)
    include_module_details = config.get("include_module_details", False)

    table = Table(box=box.SIMPLE, show_header=False, show_lines=True)
    table.add_column("Property", style="i")
    table.add_column("Value")

    if self.doc:
        if include_full_doc:
            table.add_row("Documentation", self.doc.full_doc)
        else:
            table.add_row("Description", self.doc.description)

    # module_type_md = self.module.get_type_metadata()

    if include_inputs:
        inputs_table = create_table_from_field_schemas(
            _add_required=True,
            _add_default=True,
            _show_header=True,
            _constants=None,
            **self.operation_details.inputs_schema,
        )
        table.add_row("Inputs", inputs_table)
    if include_outputs:
        outputs_table = create_table_from_field_schemas(
            _add_required=False,
            _add_default=False,
            _show_header=True,
            _constants=None,
            **self.operation_details.outputs_schema,
        )
        table.add_row("Outputs", outputs_table)

    if include_module_details:
        table.add_row("Module type", self.module_type)

        module_config = self.module.config.json(option=orjson.OPT_INDENT_2)
        conf = Syntax(
            module_config,
            "json",
            background_color="default",
        )
        table.add_row("Module config", conf)

        module_type_md = KiaraModuleTypeInfo.create_from_type_class(
            self.module_details.get_class()  # type: ignore
        )

        desc = module_type_md.documentation.description
        module_md = module_type_md.create_renderable(
            include_doc=False, include_src=False, include_config_schema=False
        )
        m_md = Group(desc, module_md)
        table.add_row("Module metadata", m_md)

    if include_src:
        table.add_row("Source code", module_type_md.process_src)

    return table

prepare_job_config(self, kiara, inputs)

Source code in kiara/models/module/operation.py

def prepare_job_config(
    self, kiara: "Kiara", inputs: Mapping[str, Any]
) -> JobConfig:

    augmented_inputs = (
        self.operation_details.get_operation_schema().augment_module_inputs(
            inputs=inputs
        )
    )

    module_inputs = self.operation_details.create_module_inputs(
        inputs=augmented_inputs
    )

    job_config = kiara.job_registry.prepare_job_config(
        manifest=self, inputs=module_inputs
    )
    return job_config

process_job_outputs(self, outputs)

Source code in kiara/models/module/operation.py

def process_job_outputs(self, outputs: ValueMap) -> ValueMap:

    op_outputs = self.operation_details.create_operation_outputs(outputs=outputs)

    value_set = ValueMapReadOnly(value_items=op_outputs, values_schema=self.outputs_schema)  # type: ignore
    return value_set

run(self, kiara, inputs)

Source code in kiara/models/module/operation.py

def run(self, kiara: "Kiara", inputs: Any) -> ValueMap:

    logger.debug("run.operation", operation_id=self.operation_id)
    job_config = self.prepare_job_config(kiara=kiara, inputs=inputs)

    job_id = kiara.job_registry.execute_job(job_config=job_config)
    outputs: ValueMap = kiara.job_registry.retrieve_result(job_id=job_id)

    result = self.process_job_outputs(outputs=outputs)

    return result

OperationConfig (KiaraModel) pydantic-model

Source code in kiara/models/module/operation.py

class OperationConfig(KiaraModel):

    doc: DocumentationMetadataModel = Field(
        description="Documentation for this operation."
    )

    @validator("doc", pre=True)
    def validate_doc(cls, value):
        return DocumentationMetadataModel.create(value)

    @abc.abstractmethod
    def retrieve_module_type(self, kiara: "Kiara") -> str:
        pass

    @abc.abstractmethod
    def retrieve_module_config(self, kiara: "Kiara") -> Mapping[str, Any]:
        pass

Attributes

doc: DocumentationMetadataModel pydantic-field required

Documentation for this operation.

retrieve_module_config(self, kiara)

Source code in kiara/models/module/operation.py

@abc.abstractmethod
def retrieve_module_config(self, kiara: "Kiara") -> Mapping[str, Any]:
    pass

retrieve_module_type(self, kiara)

Source code in kiara/models/module/operation.py

@abc.abstractmethod
def retrieve_module_type(self, kiara: "Kiara") -> str:
    pass

validate_doc(value) classmethod

Source code in kiara/models/module/operation.py

@validator("doc", pre=True)
def validate_doc(cls, value):
    return DocumentationMetadataModel.create(value)

OperationDetails (KiaraModel) pydantic-model

Source code in kiara/models/module/operation.py

class OperationDetails(KiaraModel):

    _kiara_model_id = "instance.operation_details"

    @classmethod
    def create_operation_details(cls, **details: Any):

        if PYDANTIC_USE_CONSTRUCT:
            result = cls.construct(**details)
        else:
            result = cls(**details)

        return result

    operation_id: str = Field(description="The id of the operation.")
    is_internal_operation: bool = Field(
        description="Whether this operation is mainly used kiara-internally. Helps to hide it in UIs (operation lists etc.).",
        default=False,
    )

    def _retrieve_id(self) -> str:
        return self.operation_id

    @property
    def inputs_schema(self) -> Mapping[str, ValueSchema]:
        """The input schema for this module."""

        return self.get_operation_schema().inputs_schema

    @property
    def outputs_schema(self) -> Mapping[str, ValueSchema]:
        """The input schema for this module."""

        return self.get_operation_schema().outputs_schema

    def get_operation_schema(self) -> OperationSchema:
        raise NotImplementedError()

    def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
        raise NotImplementedError()

    def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:
        raise NotImplementedError()

Attributes

inputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

The input schema for this module.

is_internal_operation: bool pydantic-field

Whether this operation is mainly used kiara-internally. Helps to hide it in UIs (operation lists etc.).

operation_id: str pydantic-field required

The id of the operation.

outputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

The input schema for this module.

create_module_inputs(self, inputs)

Source code in kiara/models/module/operation.py

def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
    raise NotImplementedError()

create_operation_details(**details) classmethod

Source code in kiara/models/module/operation.py

@classmethod
def create_operation_details(cls, **details: Any):

    if PYDANTIC_USE_CONSTRUCT:
        result = cls.construct(**details)
    else:
        result = cls(**details)

    return result

create_operation_outputs(self, outputs)

Source code in kiara/models/module/operation.py

def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:
    raise NotImplementedError()

get_operation_schema(self)

Source code in kiara/models/module/operation.py

def get_operation_schema(self) -> OperationSchema:
    raise NotImplementedError()

OperationGroupInfo (InfoModelGroup) pydantic-model

Source code in kiara/models/module/operation.py

class OperationGroupInfo(InfoModelGroup):

    _kiara_model_id = "info.operations"

    @classmethod
    def base_info_class(cls) -> Type[ItemInfo]:
        return OperationInfo

    @classmethod
    def create_from_operations(
        cls, kiara: "Kiara", group_alias: Optional[str] = None, **items: Operation
    ) -> "OperationGroupInfo":

        type_infos = {
            k: OperationInfo.create_from_operation(kiara=kiara, operation=v)
            for k, v in items.items()
        }
        data_types_info = cls.construct(group_alias=group_alias, type_infos=type_infos)
        return data_types_info

    type_name: Literal["operation_type"] = "operation_type"
    type_infos: Mapping[str, OperationInfo] = Field(
        description="The operation info instances for each type."
    )

    def create_renderable(self, **config: Any) -> RenderableType:

        by_type = config.get("by_type", False)

        if by_type:
            return self._create_renderable_by_type(**config)
        else:
            return self._create_renderable_list(**config)

    def _create_renderable_list(self, **config):

        include_internal_operations = config.get("include_internal_operations", True)
        full_doc = config.get("full_doc", False)
        filter = config.get("filter", [])

        table = Table(box=box.SIMPLE, show_header=True)
        table.add_column("Id", no_wrap=True, style="i")
        table.add_column("Type(s)", style="green")
        table.add_column("Description")

        for op_id, op_info in self.type_infos.items():

            if (
                not include_internal_operations
                and op_info.operation.operation_details.is_internal_operation
            ):
                continue

            types = op_info.operation_types

            if "custom_module" in types:
                types.remove("custom_module")

            desc_str = op_info.documentation.description
            if full_doc:
                desc = Markdown(op_info.documentation.full_doc)
            else:
                desc = Markdown(op_info.documentation.description)

            if filter:
                match = True
                for f in filter:
                    if (
                        f.lower() not in op_id.lower()
                        and f.lower() not in desc_str.lower()
                    ):
                        match = False
                        break
                if match:
                    table.add_row(op_id, ", ".join(types), desc)

            else:
                table.add_row(op_id, ", ".join(types), desc)

        return table

    def _create_renderable_by_type(self, **config):

        include_internal_operations = config.get("include_internal_operations", True)
        full_doc = config.get("full_doc", False)
        filter = config.get("filter", [])

        by_type = {}
        for op_id, op in self.type_infos.items():
            if filter:
                match = True
                for f in filter:
                    if (
                        f.lower() not in op_id.lower()
                        and f.lower() not in op.documentation.description.lower()
                    ):
                        match = False
                        break
                if not match:
                    continue
            for op_type in op.operation_types:
                by_type.setdefault(op_type, {})[op_id] = op

        table = Table(box=box.SIMPLE, show_header=True)
        table.add_column("Type", no_wrap=True, style="b green")
        table.add_column("Id", no_wrap=True, style="i")
        if full_doc:
            table.add_column("Documentation", no_wrap=False, style="i")
        else:
            table.add_column("Description", no_wrap=False, style="i")

        for operation_name in sorted(by_type.keys()):

            if operation_name == "custom_module":
                continue

            first_line_value = True
            op_infos = by_type[operation_name]

            for op_id in sorted(op_infos.keys()):
                op_info: OperationInfo = op_infos[op_id]

                if (
                    not include_internal_operations
                    and op_info.operation.operation_details.is_internal_operation
                ):
                    continue

                if full_doc:
                    desc = Markdown(op_info.documentation.full_doc)
                else:
                    desc = Markdown(op_info.documentation.description)

                row = []
                if first_line_value:
                    row.append(operation_name)
                else:
                    row.append("")

                row.append(op_id)
                row.append(desc)

                table.add_row(*row)
                first_line_value = False

        return table

Attributes

type_infos: Mapping[str, kiara.models.module.operation.OperationInfo] pydantic-field required

The operation info instances for each type.

type_name: Literal['operation_type'] pydantic-field

base_info_class() classmethod

Source code in kiara/models/module/operation.py

@classmethod
def base_info_class(cls) -> Type[ItemInfo]:
    return OperationInfo

create_from_operations(kiara, group_alias=None, **items) classmethod

Source code in kiara/models/module/operation.py

@classmethod
def create_from_operations(
    cls, kiara: "Kiara", group_alias: Optional[str] = None, **items: Operation
) -> "OperationGroupInfo":

    type_infos = {
        k: OperationInfo.create_from_operation(kiara=kiara, operation=v)
        for k, v in items.items()
    }
    data_types_info = cls.construct(group_alias=group_alias, type_infos=type_infos)
    return data_types_info

create_renderable(self, **config)

Source code in kiara/models/module/operation.py

def create_renderable(self, **config: Any) -> RenderableType:

    by_type = config.get("by_type", False)

    if by_type:
        return self._create_renderable_by_type(**config)
    else:
        return self._create_renderable_list(**config)

OperationInfo (ItemInfo) pydantic-model

Source code in kiara/models/module/operation.py

class OperationInfo(ItemInfo):

    _kiara_model_id = "info.operation"

    @classmethod
    def create_from_operation(cls, kiara: "Kiara", operation: Operation):

        module = operation.module
        module_cls = module.__class__

        authors_md = AuthorsMetadataModel.from_class(module_cls)
        properties_md = ContextMetadataModel.from_class(module_cls)

        op_types = kiara.operation_registry.find_all_operation_types(
            operation_id=operation.operation_id
        )

        op_info = OperationInfo.construct(
            type_name=operation.operation_id,
            operation_types=list(op_types),
            operation=operation,
            documentation=operation.doc,
            authors=authors_md,
            context=properties_md,
        )

        return op_info

    @classmethod
    def category_name(cls) -> str:
        return "operation"

    operation: Operation = Field(description="The operation instance.")
    operation_types: List[str] = Field(
        description="The operation types this operation belongs to."
    )

    def create_renderable(self, **config: Any) -> RenderableType:

        include_doc = config.get("include_doc", True)

        table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
        table.add_column("property", style="i")
        table.add_column("value")

        if include_doc:
            table.add_row(
                "Documentation",
                Panel(self.documentation.create_renderable(), box=box.SIMPLE),
            )
        table.add_row("Author(s)", self.authors.create_renderable(**config))
        table.add_row("Context", self.context.create_renderable(**config))

        table.add_row("Operation details", self.operation.create_renderable(**config))
        return table

Attributes

operation: Operation pydantic-field required

The operation instance.

operation_types: List[str] pydantic-field required

The operation types this operation belongs to.

category_name() classmethod

Source code in kiara/models/module/operation.py

@classmethod
def category_name(cls) -> str:
    return "operation"

create_from_operation(kiara, operation) classmethod

Source code in kiara/models/module/operation.py

@classmethod
def create_from_operation(cls, kiara: "Kiara", operation: Operation):

    module = operation.module
    module_cls = module.__class__

    authors_md = AuthorsMetadataModel.from_class(module_cls)
    properties_md = ContextMetadataModel.from_class(module_cls)

    op_types = kiara.operation_registry.find_all_operation_types(
        operation_id=operation.operation_id
    )

    op_info = OperationInfo.construct(
        type_name=operation.operation_id,
        operation_types=list(op_types),
        operation=operation,
        documentation=operation.doc,
        authors=authors_md,
        context=properties_md,
    )

    return op_info

create_renderable(self, **config)

Source code in kiara/models/module/operation.py

def create_renderable(self, **config: Any) -> RenderableType:

    include_doc = config.get("include_doc", True)

    table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
    table.add_column("property", style="i")
    table.add_column("value")

    if include_doc:
        table.add_row(
            "Documentation",
            Panel(self.documentation.create_renderable(), box=box.SIMPLE),
        )
    table.add_row("Author(s)", self.authors.create_renderable(**config))
    table.add_row("Context", self.context.create_renderable(**config))

    table.add_row("Operation details", self.operation.create_renderable(**config))
    return table

OperationSchema (InputOutputObject)

Source code in kiara/models/module/operation.py

class OperationSchema(InputOutputObject):
    def __init__(
        self, alias: str, inputs_schema: ValueSetSchema, outputs_schema: ValueSetSchema
    ):

        allow_empty_inputs = True
        allow_empty_outputs = True

        self._inputs_schema_static: ValueSetSchema = inputs_schema
        self._outputs_schema_static: ValueSetSchema = outputs_schema
        super().__init__(
            alias=alias,
            allow_empty_inputs_schema=allow_empty_inputs,
            allow_empty_outputs_schema=allow_empty_outputs,
        )

    def create_inputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:
        return self._inputs_schema_static

    def create_outputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        return self._outputs_schema_static

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/models/module/operation.py

def create_inputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:
    return self._inputs_schema_static

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/models/module/operation.py

def create_outputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    return self._outputs_schema_static

OperationTypeClassesInfo (TypeInfoModelGroup) pydantic-model

Source code in kiara/models/module/operation.py

class OperationTypeClassesInfo(TypeInfoModelGroup):

    _kiara_model_id = "info.operation_types"

    @classmethod
    def base_info_class(cls) -> Type[TypeInfo]:
        return OperationTypeInfo

    type_name: Literal["operation_type"] = "operation_type"
    type_infos: Mapping[str, OperationTypeInfo] = Field(
        description="The operation info instances for each type."
    )

Attributes

type_infos: Mapping[str, kiara.models.module.operation.OperationTypeInfo] pydantic-field required

The operation info instances for each type.

type_name: Literal['operation_type'] pydantic-field

base_info_class() classmethod

Source code in kiara/models/module/operation.py

@classmethod
def base_info_class(cls) -> Type[TypeInfo]:
    return OperationTypeInfo

OperationTypeInfo (TypeInfo) pydantic-model

Source code in kiara/models/module/operation.py

class OperationTypeInfo(TypeInfo):

    _kiara_model_id = "info.operation_type"

    @classmethod
    def create_from_type_class(
        cls, type_cls: Type["OperationType"]
    ) -> "OperationTypeInfo":

        authors_md = AuthorsMetadataModel.from_class(type_cls)
        doc = DocumentationMetadataModel.from_class_doc(type_cls)
        python_class = PythonClass.from_class(type_cls)
        properties_md = ContextMetadataModel.from_class(type_cls)

        return OperationTypeInfo.construct(
            **{
                "type_name": type_cls._operation_type_name,  # type: ignore
                "documentation": doc,
                "authors": authors_md,
                "context": properties_md,
                "python_class": python_class,
            }
        )

    @classmethod
    def base_class(self) -> Type["OperationType"]:
        from kiara.operations import OperationType

        return OperationType

    @classmethod
    def category_name(cls) -> str:
        return "operation_type"

    def _retrieve_id(self) -> str:
        return self.type_name

    def _retrieve_data_to_hash(self) -> Any:
        return self.type_name

base_class() classmethod

Source code in kiara/models/module/operation.py

@classmethod
def base_class(self) -> Type["OperationType"]:
    from kiara.operations import OperationType

    return OperationType

category_name() classmethod

Source code in kiara/models/module/operation.py

@classmethod
def category_name(cls) -> str:
    return "operation_type"

create_from_type_class(type_cls) classmethod

Source code in kiara/models/module/operation.py

@classmethod
def create_from_type_class(
    cls, type_cls: Type["OperationType"]
) -> "OperationTypeInfo":

    authors_md = AuthorsMetadataModel.from_class(type_cls)
    doc = DocumentationMetadataModel.from_class_doc(type_cls)
    python_class = PythonClass.from_class(type_cls)
    properties_md = ContextMetadataModel.from_class(type_cls)

    return OperationTypeInfo.construct(
        **{
            "type_name": type_cls._operation_type_name,  # type: ignore
            "documentation": doc,
            "authors": authors_md,
            "context": properties_md,
            "python_class": python_class,
        }
    )

PipelineOperationConfig (OperationConfig) pydantic-model

Source code in kiara/models/module/operation.py

class PipelineOperationConfig(OperationConfig):

    _kiara_model_id = "instance.operation_config.pipeline"

    pipeline_name: str = Field(description="The pipeline id.")
    pipeline_config: Mapping[str, Any] = Field(description="The pipeline config data.")
    module_map: Dict[str, Any] = Field(
        description="A lookup map to resolves module names to operations.",
        default_factory=dict,
    )
    metadata: Mapping[str, Any] = Field(
        description="Additional metadata for the pipeline.", default_factory=dict
    )

    @validator("pipeline_config")
    def validate_pipeline_config(cls, value):
        # TODO
        assert isinstance(value, Mapping)
        assert "steps" in value.keys()

        return value

    def retrieve_module_type(self, kiara: "Kiara") -> str:
        return "pipeline"

    def retrieve_module_config(self, kiara: "Kiara") -> Mapping[str, Any]:

        # using _from_config here because otherwise we'd enter an infinite loop
        pipeline_config = PipelineConfig._from_config(
            pipeline_name=self.pipeline_name,
            data=self.pipeline_config,
            kiara=kiara,
            module_map=self.module_map,
        )
        return pipeline_config.dict()

    @property
    def required_module_types(self) -> Iterable[str]:

        return [step["module_type"] for step in self.pipeline_config["steps"]]

    def __repr__(self):

        return f"{self.__class__.__name__}(pipeline_name={self.pipeline_name} required_modules={list(self.required_module_types)} instance_id={self.instance_id}, category={self.model_type_id}, fields=[{', '.join(self.__fields__.keys())}])"

Attributes

metadata: Mapping[str, Any] pydantic-field

Additional metadata for the pipeline.

module_map: Dict[str, Any] pydantic-field

A lookup map to resolves module names to operations.

pipeline_config: Mapping[str, Any] pydantic-field required

The pipeline config data.

pipeline_name: str pydantic-field required

The pipeline id.

required_module_types: Iterable[str] property readonly

retrieve_module_config(self, kiara)

Source code in kiara/models/module/operation.py

def retrieve_module_config(self, kiara: "Kiara") -> Mapping[str, Any]:

    # using _from_config here because otherwise we'd enter an infinite loop
    pipeline_config = PipelineConfig._from_config(
        pipeline_name=self.pipeline_name,
        data=self.pipeline_config,
        kiara=kiara,
        module_map=self.module_map,
    )
    return pipeline_config.dict()

retrieve_module_type(self, kiara)

Source code in kiara/models/module/operation.py

def retrieve_module_type(self, kiara: "Kiara") -> str:
    return "pipeline"

validate_pipeline_config(value) classmethod

Source code in kiara/models/module/operation.py

@validator("pipeline_config")
def validate_pipeline_config(cls, value):
    # TODO
    assert isinstance(value, Mapping)
    assert "steps" in value.keys()

    return value

persistence

Classes

ByteProvisioningStrategy (Enum)

An enumeration.

Source code in kiara/models/module/persistence.py

class ByteProvisioningStrategy(Enum):

    INLINE = "INLINE"
    BYTES = "bytes"
    FILE_PATH_MAP = "link_map"
    LINK_FOLDER = "folder"
    COPIED_FOLDER = "copied_folder"

BYTES

COPIED_FOLDER

FILE_PATH_MAP

INLINE

LINK_FOLDER

BytesAliasStructure (BaseModel) pydantic-model

Source code in kiara/models/module/persistence.py

class BytesAliasStructure(BaseModel):

    data_type: str = Field(description="The data type.")
    data_type_config: Mapping[str, Any] = Field(description="The data type config.")
    chunk_id_map: Mapping[str, List[str]] = Field(
        description="References to byte arrays, Keys are field names, values are a list of hash-ids that the data is composed of.",
        default_factory=dict,
    )

Attributes

chunk_id_map: Mapping[str, List[str]] pydantic-field

References to byte arrays, Keys are field names, values are a list of hash-ids that the data is composed of.

data_type: str pydantic-field required

The data type.

data_type_config: Mapping[str, Any] pydantic-field required

The data type config.

BytesStructure (BaseModel) pydantic-model

A data structure that

Source code in kiara/models/module/persistence.py

class BytesStructure(BaseModel):
    """A data structure that"""

    data_type: str = Field(description="The data type.")
    data_type_config: Mapping[str, Any] = Field(description="The data type config.")
    chunk_map: Mapping[str, List[Union[str, bytes]]] = Field(
        description="References to byte arrays, Keys are field names, values are a list of hash-ids that the data is composed of.",
        default_factory=dict,
    )

    def provision_as_folder(self, copy_files: bool = False) -> Path:
        pass

Attributes

chunk_map: Mapping[str, List[Union[str, bytes]]] pydantic-field

References to byte arrays, Keys are field names, values are a list of hash-ids that the data is composed of.

data_type: str pydantic-field required

The data type.

data_type_config: Mapping[str, Any] pydantic-field required

The data type config.

provision_as_folder(self, copy_files=False)

Source code in kiara/models/module/persistence.py

def provision_as_folder(self, copy_files: bool = False) -> Path:
    pass

pipeline special

Classes

PipelineConfig (KiaraModuleConfig) pydantic-model

A class to hold the configuration for a [PipelineModule][kiara.pipeline.module.PipelineModule].

If you want to control the pipeline input and output names, you need to have to provide a map that uses the autogenerated field name ([step_id]__[alias] -- 2 underscores!!) as key, and the desired field name as value. The reason that schema for the autogenerated field names exist is that it's hard to ensure the uniqueness of each field; some steps can have the same input field names, but will need different input values. In some cases, some inputs of different steps need the same input. Those sorts of things. So, to make sure that we always use the right values, I chose to implement a conservative default approach, accepting that in some cases the user will be prompted for duplicate inputs for the same value.

To remedy that, the pipeline creator has the option to manually specify a mapping to rename some or all of the input/output fields.

Further, because in a lot of cases there won't be any overlapping fields, the creator can specify auto, in which case Kiara will automatically create a mapping that tries to map autogenerated field names to the shortest possible names for each case.

Examples:

Configuration for a pipeline module that functions as a nand logic gate (in Python):

and_step = PipelineStepConfig(module_type="and", step_id="and")
not_step = PipelineStepConfig(module_type="not", step_id="not", input_links={"a": ["and.y"]}
nand_p_conf = PipelineConfig(doc="Returns 'False' if both inputs are 'True'.",
                    steps=[and_step, not_step],
                    input_aliases={
                        "and__a": "a",
                        "and__b": "b"
                    },
                    output_aliases={
                        "not__y": "y"
                    }}

Or, the same thing in json:

{
  "module_type_name": "nand",
  "doc": "Returns 'False' if both inputs are 'True'.",
  "steps": [
    {
      "module_type": "and",
      "step_id": "and"
    },
    {
      "module_type": "not",
      "step_id": "not",
      "input_links": {
        "a": "and.y"
      }
    }
  ],
  "input_aliases": {
    "and__a": "a",
    "and__b": "b"
  },
  "output_aliases": {
    "not__y": "y"
  }
}

Source code in kiara/models/module/pipeline/__init__.py

class PipelineConfig(KiaraModuleConfig):
    """A class to hold the configuration for a [PipelineModule][kiara.pipeline.module.PipelineModule].

    If you want to control the pipeline input and output names, you need to have to provide a map that uses the
    autogenerated field name ([step_id]__[alias] -- 2 underscores!!) as key, and the desired field name
    as value. The reason that schema for the autogenerated field names exist is that it's hard to ensure
    the uniqueness of each field; some steps can have the same input field names, but will need different input
    values. In some cases, some inputs of different steps need the same input. Those sorts of things.
    So, to make sure that we always use the right values, I chose to implement a conservative default approach,
    accepting that in some cases the user will be prompted for duplicate inputs for the same value.

    To remedy that, the pipeline creator has the option to manually specify a mapping to rename some or all of
    the input/output fields.

    Further, because in a lot of cases there won't be any overlapping fields, the creator can specify ``auto``,
    in which case *Kiara* will automatically create a mapping that tries to map autogenerated field names
    to the shortest possible names for each case.

    Examples:

        Configuration for a pipeline module that functions as a ``nand`` logic gate (in Python):

        ``` python
        and_step = PipelineStepConfig(module_type="and", step_id="and")
        not_step = PipelineStepConfig(module_type="not", step_id="not", input_links={"a": ["and.y"]}
        nand_p_conf = PipelineConfig(doc="Returns 'False' if both inputs are 'True'.",
                            steps=[and_step, not_step],
                            input_aliases={
                                "and__a": "a",
                                "and__b": "b"
                            },
                            output_aliases={
                                "not__y": "y"
                            }}
        ```

        Or, the same thing in json:

        ``` json
        {
          "module_type_name": "nand",
          "doc": "Returns 'False' if both inputs are 'True'.",
          "steps": [
            {
              "module_type": "and",
              "step_id": "and"
            },
            {
              "module_type": "not",
              "step_id": "not",
              "input_links": {
                "a": "and.y"
              }
            }
          ],
          "input_aliases": {
            "and__a": "a",
            "and__b": "b"
          },
          "output_aliases": {
            "not__y": "y"
          }
        }
        ```
    """

    _kiara_model_id = "instance.module_config.pipeline"

    @classmethod
    def from_file(
        cls,
        path: str,
        kiara: Optional["Kiara"] = None,
        # module_map: Optional[Mapping[str, Any]] = None,
    ):

        data = get_data_from_file(path)
        pipeline_name = data.pop("pipeline_name", None)
        if pipeline_name is None:
            pipeline_name = os.path.basename(path)

        return cls.from_config(pipeline_name=pipeline_name, data=data, kiara=kiara)

    @classmethod
    def from_config(
        cls,
        pipeline_name: str,
        data: Mapping[str, Any],
        kiara: Optional["Kiara"] = None,
        # module_map: Optional[Mapping[str, Any]] = None,
    ):

        if kiara is None:
            from kiara.context import Kiara

            kiara = Kiara.instance()

        if not kiara.operation_registry.is_initialized:
            kiara.operation_registry.operations  # noqa

        config = cls._from_config(pipeline_name=pipeline_name, data=data, kiara=kiara)
        return config

    @classmethod
    def _from_config(
        cls,
        pipeline_name: str,
        data: Mapping[str, Any],
        kiara: "Kiara",
        module_map: Optional[Mapping[str, Any]] = None,
    ):

        data = dict(data)
        steps = data.pop("steps")
        steps = PipelineStep.create_steps(*steps, kiara=kiara, module_map=module_map)
        data["steps"] = steps
        if not data.get("input_aliases"):
            data["input_aliases"] = create_input_alias_map(steps)
        if not data.get("output_aliases"):
            data["output_aliases"] = create_output_alias_map(steps)

        result = cls(pipeline_name=pipeline_name, **data)

        return result

    class Config:
        extra = Extra.ignore
        validate_assignment = True

    pipeline_name: str = Field(description="The name of this pipeline.")
    steps: List[PipelineStep] = Field(
        description="A list of steps/modules of this pipeline, and their connections.",
    )
    input_aliases: Dict[str, str] = Field(
        description="A map of input aliases, with the calculated (<step_id>__<input_name> -- double underscore!) name as key, and a string (the resulting workflow input alias) as value. Check the documentation for the config class for which marker strings can be used to automatically create this map if possible.",
    )
    output_aliases: Dict[str, str] = Field(
        description="A map of output aliases, with the calculated (<step_id>__<output_name> -- double underscore!) name as key, and a string (the resulting workflow output alias) as value.  Check the documentation for the config class for which marker strings can be used to automatically create this map if possible.",
    )
    doc: str = Field(
        default="-- n/a --", description="Documentation about what the pipeline does."
    )
    context: Dict[str, Any] = Field(
        default_factory=dict, description="Metadata for this workflow."
    )
    _structure: Optional["PipelineStructure"] = PrivateAttr(default=None)

    @validator("steps", pre=True)
    def _validate_steps(cls, v):

        if not v:
            raise ValueError(f"Invalid type for 'steps' value: {type(v)}")

        steps = []
        for step in v:
            if not step:
                raise ValueError("No step data provided.")
            if isinstance(step, PipelineStep):
                steps.append(step)
            elif isinstance(step, Mapping):
                steps.append(PipelineStep(**step))
            else:
                raise TypeError(step)
        return steps

    @property
    def structure(self) -> "PipelineStructure":

        if self._structure is not None:
            return self._structure

        from kiara.models.module.pipeline.structure import PipelineStructure

        self._structure = PipelineStructure(pipeline_config=self)
        return self._structure

    def create_renderable(self, **config: Any) -> RenderableType:

        return create_table_from_model_object(self, exclude_fields={"steps"})

    # def create_input_alias_map(self) -> Dict[str, str]:
    #
    #     aliases: Dict[str, List[str]] = {}
    #     for step in self.steps:
    #         field_names = step.module.input_names
    #         for field_name in field_names:
    #             aliases.setdefault(field_name, []).append(step.step_id)
    #
    #     result: Dict[str, str] = {}
    #     for field_name, step_ids in aliases.items():
    #         for step_id in step_ids:
    #             generated = generate_pipeline_endpoint_name(step_id, field_name)
    #             result[generated] = generated
    #
    #     return result
    #
    # def create_output_alias_map(self) -> Dict[str, str]:
    #
    #     aliases: Dict[str, List[str]] = {}
    #     for step in self.steps:
    #         field_names = step.module.input_names
    #         for field_name in field_names:
    #             aliases.setdefault(field_name, []).append(step.step_id)
    #
    #     result: Dict[str, str] = {}
    #     for field_name, step_ids in aliases.items():
    #         for step_id in step_ids:
    #             generated = generate_pipeline_endpoint_name(step_id, field_name)
    #             result[generated] = generated
    #
    #     return result

Attributes

context: Dict[str, Any] pydantic-field

Metadata for this workflow.

doc: str pydantic-field

Documentation about what the pipeline does.

input_aliases: Dict[str, str] pydantic-field required

A map of input aliases, with the calculated (__ -- double underscore!) name as key, and a string (the resulting workflow input alias) as value. Check the documentation for the config class for which marker strings can be used to automatically create this map if possible.

output_aliases: Dict[str, str] pydantic-field required

A map of output aliases, with the calculated (__ -- double underscore!) name as key, and a string (the resulting workflow output alias) as value. Check the documentation for the config class for which marker strings can be used to automatically create this map if possible.

pipeline_name: str pydantic-field required

The name of this pipeline.

steps: List[kiara.models.module.pipeline.PipelineStep] pydantic-field required

A list of steps/modules of this pipeline, and their connections.

structure: PipelineStructure property readonly

Config

Source code in kiara/models/module/pipeline/__init__.py

class Config:
    extra = Extra.ignore
    validate_assignment = True

extra

validate_assignment

create_renderable(self, **config)

Source code in kiara/models/module/pipeline/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:

    return create_table_from_model_object(self, exclude_fields={"steps"})

from_config(pipeline_name, data, kiara=None) classmethod

Source code in kiara/models/module/pipeline/__init__.py

@classmethod
def from_config(
    cls,
    pipeline_name: str,
    data: Mapping[str, Any],
    kiara: Optional["Kiara"] = None,
    # module_map: Optional[Mapping[str, Any]] = None,
):

    if kiara is None:
        from kiara.context import Kiara

        kiara = Kiara.instance()

    if not kiara.operation_registry.is_initialized:
        kiara.operation_registry.operations  # noqa

    config = cls._from_config(pipeline_name=pipeline_name, data=data, kiara=kiara)
    return config

from_file(path, kiara=None) classmethod

Source code in kiara/models/module/pipeline/__init__.py

@classmethod
def from_file(
    cls,
    path: str,
    kiara: Optional["Kiara"] = None,
    # module_map: Optional[Mapping[str, Any]] = None,
):

    data = get_data_from_file(path)
    pipeline_name = data.pop("pipeline_name", None)
    if pipeline_name is None:
        pipeline_name = os.path.basename(path)

    return cls.from_config(pipeline_name=pipeline_name, data=data, kiara=kiara)

PipelineStep (Manifest) pydantic-model

A step within a pipeline-structure, includes information about it's connection(s) and other metadata.

Source code in kiara/models/module/pipeline/__init__.py

class PipelineStep(Manifest):
    """A step within a pipeline-structure, includes information about it's connection(s) and other metadata."""

    _kiara_model_id = "instance.pipeline_step"

    class Config:
        validate_assignment = True
        extra = Extra.forbid

    @classmethod
    def create_steps(
        cls,
        *steps: Mapping[str, Any],
        kiara: "Kiara",
        module_map: Optional[Mapping[str, Any]] = None,
    ) -> List["PipelineStep"]:

        if module_map is None:
            module_map = {}
        else:
            module_map = dict(module_map)

        if kiara.operation_registry.is_initialized:
            for op_id, op in kiara.operation_registry.operations.items():
                module_map[op_id] = {
                    "module_type": op.module_type,
                    "module_config": op.module_config,
                }

        result: List[PipelineStep] = []

        for step in steps:

            module_type = step.get("module_type", None)
            if not module_type:
                raise ValueError("Can't create step, no 'module_type' specified.")

            module_config = step.get("module_config", {})

            if module_type not in kiara.module_type_names:
                if module_type in module_map.keys():
                    resolved_module_type = module_map[module_type]["module_type"]
                    resolved_module_config = module_map[module_type]["module_config"]
                    manifest = kiara.create_manifest(
                        module_or_operation=resolved_module_type,
                        config=resolved_module_config,
                    )
                else:
                    raise Exception(f"Can't resolve module type: {module_type}")
            else:
                manifest = kiara.create_manifest(
                    module_or_operation=module_type, config=module_config
                )
                resolved_module_type = module_type
                resolved_module_config = module_config

            module = kiara.create_module(manifest=manifest)

            step_id = step.get("step_id", None)
            if not step_id:
                raise ValueError("Can't create step, no 'step_id' specified.")

            input_links = {}
            for input_field, sources in step.get("input_links", {}).items():
                if isinstance(sources, str):
                    sources = [sources]
                    input_links[input_field] = sources

            # TODO: do we really need the deepcopy here?
            _s = PipelineStep(
                step_id=step_id,
                module_type=resolved_module_type,
                module_config=dict(resolved_module_config),
                input_links=input_links,  # type: ignore
                module_details=KiaraModuleClass.from_module(module=module),
            )
            _s._module = module
            result.append(_s)

        return result

    @validator("step_id")
    def _validate_step_id(cls, v):

        assert isinstance(v, str)
        if "." in v:
            raise ValueError("Step ids can't contain '.' characters.")

        return v

    step_id: str = Field(
        description="Locally unique id (within a pipeline) of this step."
    )

    module_type: str = Field(description="The module type.")
    module_config: Dict[str, Any] = Field(
        description="The module config.", default_factory=dict
    )
    # required: bool = Field(
    #     description="Whether this step is required within the workflow.\n\nIn some cases, when none of the pipeline outputs have a required input that connects to a step, then it is not necessary for this step to have been executed, even if it is placed before a step in the execution hierarchy. This also means that the pipeline inputs that are connected to this step might not be required.",
    #     default=True,
    # )
    # processing_stage: Optional[int] = Field(
    #     default=None,
    #     description="The stage number this step is executed within the pipeline.",
    # )
    input_links: Mapping[str, List[StepValueAddress]] = Field(
        description="The links that connect to inputs of the module.",
        default_factory=list,
    )
    module_details: KiaraModuleClass = Field(
        description="The class of the underlying module."
    )
    _module: Optional["KiaraModule"] = PrivateAttr(default=None)

    @root_validator(pre=True)
    def create_step_id(cls, values):

        if "module_type" not in values:
            raise ValueError("No 'module_type' specified.")
        if "step_id" not in values or not values["step_id"]:
            values["step_id"] = slugify(values["module_type"])

        return values

    @validator("step_id")
    def ensure_valid_id(cls, v):

        # TODO: check with regex
        if "." in v or " " in v:
            raise ValueError(
                f"Step id can't contain special characters or whitespaces: {v}"
            )

        return v

    @validator("module_config", pre=True)
    def ensure_dict(cls, v):

        if v is None:
            v = {}
        return v

    @validator("input_links", pre=True)
    def ensure_input_links_valid(cls, v):

        if v is None:
            v = {}

        result = {}
        for input_name, output in v.items():

            input_links = ensure_step_value_addresses(
                default_field_name=input_name, link=output
            )
            result[input_name] = input_links

        return result

    @property
    def module(self) -> "KiaraModule":
        if self._module is None:
            m_cls = self.module_details.get_class()
            self._module = m_cls(module_config=self.module_config)
        return self._module

    def __repr__(self):

        return f"{self.__class__.__name__}(step_id={self.step_id} module_type={self.module_type})"

    def __str__(self):
        return f"step: {self.step_id} (module: {self.module_type})"

Attributes

input_links: Mapping[str, List[kiara.models.module.pipeline.value_refs.StepValueAddress]] pydantic-field

The links that connect to inputs of the module.

module: KiaraModule property readonly

module_details: KiaraModuleClass pydantic-field required

The class of the underlying module.

step_id: str pydantic-field required

Locally unique id (within a pipeline) of this step.

Config

Source code in kiara/models/module/pipeline/__init__.py

class Config:
    validate_assignment = True
    extra = Extra.forbid

extra

validate_assignment

create_step_id(values) classmethod

Source code in kiara/models/module/pipeline/__init__.py

@root_validator(pre=True)
def create_step_id(cls, values):

    if "module_type" not in values:
        raise ValueError("No 'module_type' specified.")
    if "step_id" not in values or not values["step_id"]:
        values["step_id"] = slugify(values["module_type"])

    return values

create_steps(*steps, *, kiara, module_map=None) classmethod

Source code in kiara/models/module/pipeline/__init__.py

@classmethod
def create_steps(
    cls,
    *steps: Mapping[str, Any],
    kiara: "Kiara",
    module_map: Optional[Mapping[str, Any]] = None,
) -> List["PipelineStep"]:

    if module_map is None:
        module_map = {}
    else:
        module_map = dict(module_map)

    if kiara.operation_registry.is_initialized:
        for op_id, op in kiara.operation_registry.operations.items():
            module_map[op_id] = {
                "module_type": op.module_type,
                "module_config": op.module_config,
            }

    result: List[PipelineStep] = []

    for step in steps:

        module_type = step.get("module_type", None)
        if not module_type:
            raise ValueError("Can't create step, no 'module_type' specified.")

        module_config = step.get("module_config", {})

        if module_type not in kiara.module_type_names:
            if module_type in module_map.keys():
                resolved_module_type = module_map[module_type]["module_type"]
                resolved_module_config = module_map[module_type]["module_config"]
                manifest = kiara.create_manifest(
                    module_or_operation=resolved_module_type,
                    config=resolved_module_config,
                )
            else:
                raise Exception(f"Can't resolve module type: {module_type}")
        else:
            manifest = kiara.create_manifest(
                module_or_operation=module_type, config=module_config
            )
            resolved_module_type = module_type
            resolved_module_config = module_config

        module = kiara.create_module(manifest=manifest)

        step_id = step.get("step_id", None)
        if not step_id:
            raise ValueError("Can't create step, no 'step_id' specified.")

        input_links = {}
        for input_field, sources in step.get("input_links", {}).items():
            if isinstance(sources, str):
                sources = [sources]
                input_links[input_field] = sources

        # TODO: do we really need the deepcopy here?
        _s = PipelineStep(
            step_id=step_id,
            module_type=resolved_module_type,
            module_config=dict(resolved_module_config),
            input_links=input_links,  # type: ignore
            module_details=KiaraModuleClass.from_module(module=module),
        )
        _s._module = module
        result.append(_s)

    return result

ensure_dict(v) classmethod

Source code in kiara/models/module/pipeline/__init__.py

@validator("module_config", pre=True)
def ensure_dict(cls, v):

    if v is None:
        v = {}
    return v

ensure_input_links_valid(v) classmethod

Source code in kiara/models/module/pipeline/__init__.py

@validator("input_links", pre=True)
def ensure_input_links_valid(cls, v):

    if v is None:
        v = {}

    result = {}
    for input_name, output in v.items():

        input_links = ensure_step_value_addresses(
            default_field_name=input_name, link=output
        )
        result[input_name] = input_links

    return result

ensure_valid_id(v) classmethod

Source code in kiara/models/module/pipeline/__init__.py

@validator("step_id")
def ensure_valid_id(cls, v):

    # TODO: check with regex
    if "." in v or " " in v:
        raise ValueError(
            f"Step id can't contain special characters or whitespaces: {v}"
        )

    return v

StepStatus (Enum)

Enum to describe the state of a workflow.

Source code in kiara/models/module/pipeline/__init__.py

class StepStatus(Enum):
    """Enum to describe the state of a workflow."""

    INPUTS_INVALID = "inputs_invalid"
    INPUTS_READY = "inputs_ready"
    RESULTS_READY = "results_ready"

INPUTS_INVALID

INPUTS_READY

RESULTS_READY

create_input_alias_map(steps)

Source code in kiara/models/module/pipeline/__init__.py

def create_input_alias_map(steps: Iterable[PipelineStep]) -> Dict[str, str]:

    aliases: Dict[str, str] = {}
    for step in steps:
        field_names = step.module.input_names
        for field_name in field_names:
            alias = generate_pipeline_endpoint_name(
                step_id=step.step_id, value_name=field_name
            )
            assert alias not in aliases.keys()
            aliases[f"{step.step_id}.{field_name}"] = alias

    return aliases

create_output_alias_map(steps)

Source code in kiara/models/module/pipeline/__init__.py

def create_output_alias_map(steps: Iterable[PipelineStep]) -> Dict[str, str]:

    aliases: Dict[str, str] = {}
    for step in steps:
        field_names = step.module.output_names
        for field_name in field_names:
            alias = generate_pipeline_endpoint_name(
                step_id=step.step_id, value_name=field_name
            )
            assert alias not in aliases.keys()
            aliases[f"{step.step_id}.{field_name}"] = alias

    return aliases

generate_pipeline_endpoint_name(step_id, value_name)

Source code in kiara/models/module/pipeline/__init__.py

def generate_pipeline_endpoint_name(step_id: str, value_name: str):

    return f"{step_id}__{value_name}"

Modules

controller

logger

Classes

PipelineController (PipelineListener)

Source code in kiara/models/module/pipeline/controller.py

class PipelineController(PipelineListener):

    pass

SinglePipelineBatchController (SinglePipelineController)

A [PipelineController][kiara.models.modules.pipeline.controller.PipelineController] that executes all pipeline steps non-interactively.

This is the default implementation of a PipelineController, and probably the most simple implementation of one. It waits until all inputs are set, after which it executes all pipeline steps in the required order.

Parameters:

Name	Type	Description	Default
pipeline	Pipeline	the pipeline to control	required
auto_process	bool	whether to automatically start processing the pipeline as soon as the input set is valid	True

Source code in kiara/models/module/pipeline/controller.py

class SinglePipelineBatchController(SinglePipelineController):
    """A [PipelineController][kiara.models.modules.pipeline.controller.PipelineController] that executes all pipeline steps non-interactively.

    This is the default implementation of a ``PipelineController``, and probably the most simple implementation of one.
    It waits until all inputs are set, after which it executes all pipeline steps in the required order.

    Arguments:
        pipeline: the pipeline to control
        auto_process: whether to automatically start processing the pipeline as soon as the input set is valid
    """

    def __init__(
        self,
        pipeline: Pipeline,
        job_registry: JobRegistry,
        auto_process: bool = True,
    ):

        self._auto_process: bool = auto_process
        self._is_running: bool = False
        super().__init__(pipeline=pipeline, job_registry=job_registry)

    @property
    def auto_process(self) -> bool:
        return self._auto_process

    @auto_process.setter
    def auto_process(self, auto_process: bool):
        self._auto_process = auto_process

    def process_pipeline(self):

        log = logger.bind(pipeline_id=self.pipeline.pipeline_id)
        if self._is_running:
            log.debug(
                "ignore.pipeline_process",
                reason="Pipeline already running.",
            )
            raise Exception("Pipeline already running.")

        log.debug("execute.pipeline")
        self._is_running = True
        try:
            for idx, stage in enumerate(
                self.pipeline.structure.processing_stages, start=1
            ):

                log.debug(
                    "execute.pipeline.stage",
                    stage=idx,
                )

                job_ids = {}
                for step_id in stage:

                    log.debug(
                        "execute.pipeline.step",
                        step_id=step_id,
                    )

                    try:
                        job_id = self.process_step(step_id)
                        job_ids[step_id] = job_id
                    except Exception as e:
                        # TODO: cancel running jobs?
                        if is_debug():
                            import traceback

                            traceback.print_exc()
                        log.error(
                            "error.processing.pipeline",
                            step_id=step_id,
                            error=e,
                        )
                        return False

                self.set_processing_results(job_ids=job_ids)
                log.debug(
                    "execute_finished.pipeline.stage",
                    stage=idx,
                )

        finally:
            self._is_running = False

        log.debug("execute_finished.pipeline")

auto_process: bool property writable

process_pipeline(self)

Source code in kiara/models/module/pipeline/controller.py

def process_pipeline(self):

    log = logger.bind(pipeline_id=self.pipeline.pipeline_id)
    if self._is_running:
        log.debug(
            "ignore.pipeline_process",
            reason="Pipeline already running.",
        )
        raise Exception("Pipeline already running.")

    log.debug("execute.pipeline")
    self._is_running = True
    try:
        for idx, stage in enumerate(
            self.pipeline.structure.processing_stages, start=1
        ):

            log.debug(
                "execute.pipeline.stage",
                stage=idx,
            )

            job_ids = {}
            for step_id in stage:

                log.debug(
                    "execute.pipeline.step",
                    step_id=step_id,
                )

                try:
                    job_id = self.process_step(step_id)
                    job_ids[step_id] = job_id
                except Exception as e:
                    # TODO: cancel running jobs?
                    if is_debug():
                        import traceback

                        traceback.print_exc()
                    log.error(
                        "error.processing.pipeline",
                        step_id=step_id,
                        error=e,
                    )
                    return False

            self.set_processing_results(job_ids=job_ids)
            log.debug(
                "execute_finished.pipeline.stage",
                stage=idx,
            )

    finally:
        self._is_running = False

    log.debug("execute_finished.pipeline")

SinglePipelineController (PipelineController)

Source code in kiara/models/module/pipeline/controller.py

class SinglePipelineController(PipelineController):
    def __init__(self, pipeline: Pipeline, job_registry: JobRegistry):

        self._pipeline: Pipeline = pipeline
        self._job_registry: JobRegistry = job_registry
        self._pipeline.add_listener(self)
        self._pipeline_details: Optional[PipelineDetails] = None

    @property
    def pipeline(self) -> Pipeline:
        return self._pipeline

    def current_pipeline_state(self) -> PipelineDetails:

        if self._pipeline_details is None:
            self._pipeline_details = self.pipeline.get_pipeline_details()
        return self._pipeline_details

    def can_be_processed(self, step_id: str) -> bool:
        """Check whether the step with the provided id is ready to be processed."""

        pipeline_state = self.current_pipeline_state()
        step_state = pipeline_state.step_states[step_id]

        return not step_state.invalid_details

    def can_be_skipped(self, step_id: str) -> bool:
        """Check whether the processing of a step can be skipped."""

        required = self.pipeline.structure.step_is_required(step_id=step_id)
        if required:
            required = self.can_be_processed(step_id)
        return required

    def _pipeline_event_occurred(self, event: PipelineEvent):

        self._pipeline_details = None

    def set_processing_results(self, job_ids: Mapping[str, uuid.UUID]):

        self._job_registry.wait_for(*job_ids.values())

        combined_outputs = {}
        for step_id, job_id in job_ids.items():
            record = self._job_registry.get_job_record_in_session(job_id=job_id)
            combined_outputs[step_id] = record.outputs

        self.pipeline.set_multiple_step_outputs(
            changed_outputs=combined_outputs, notify_listeners=True
        )

    def pipeline_is_ready(self) -> bool:
        """Return whether the pipeline is ready to be processed.

        A ``True`` result means that all pipeline inputs are set with valid values, and therefore every step within the
        pipeline can be processed.

        Returns:
            whether the pipeline can be processed as a whole (``True``) or not (``False``)
        """

        pipeline_inputs = self.pipeline._all_values.get_alias("pipeline.inputs")
        assert pipeline_inputs is not None
        return pipeline_inputs.all_items_valid

    def process_step(self, step_id: str, wait: bool = False) -> uuid.UUID:
        """Kick off processing for the step with the provided id.

        Arguments:
            step_id: the id of the step that should be started
        """

        job_config = self.pipeline.create_job_config_for_step(step_id)

        job_id = self._job_registry.execute_job(job_config=job_config)
        # job_id = self._processor.create_job(job_config=job_config)
        # self._processor.queue_job(job_id=job_id)

        if wait:
            self._job_registry.wait_for(job_id)

        return job_id

pipeline: Pipeline property readonly

Methods

can_be_processed(self, step_id)

Check whether the step with the provided id is ready to be processed.

Source code in kiara/models/module/pipeline/controller.py

def can_be_processed(self, step_id: str) -> bool:
    """Check whether the step with the provided id is ready to be processed."""

    pipeline_state = self.current_pipeline_state()
    step_state = pipeline_state.step_states[step_id]

    return not step_state.invalid_details

can_be_skipped(self, step_id)

Check whether the processing of a step can be skipped.

Source code in kiara/models/module/pipeline/controller.py

def can_be_skipped(self, step_id: str) -> bool:
    """Check whether the processing of a step can be skipped."""

    required = self.pipeline.structure.step_is_required(step_id=step_id)
    if required:
        required = self.can_be_processed(step_id)
    return required

current_pipeline_state(self)

Source code in kiara/models/module/pipeline/controller.py

def current_pipeline_state(self) -> PipelineDetails:

    if self._pipeline_details is None:
        self._pipeline_details = self.pipeline.get_pipeline_details()
    return self._pipeline_details

pipeline_is_ready(self)

Return whether the pipeline is ready to be processed.

A True result means that all pipeline inputs are set with valid values, and therefore every step within the pipeline can be processed.

Returns:

Type	Description
bool	whether the pipeline can be processed as a whole (True) or not (False)

Source code in kiara/models/module/pipeline/controller.py

def pipeline_is_ready(self) -> bool:
    """Return whether the pipeline is ready to be processed.

    A ``True`` result means that all pipeline inputs are set with valid values, and therefore every step within the
    pipeline can be processed.

    Returns:
        whether the pipeline can be processed as a whole (``True``) or not (``False``)
    """

    pipeline_inputs = self.pipeline._all_values.get_alias("pipeline.inputs")
    assert pipeline_inputs is not None
    return pipeline_inputs.all_items_valid

process_step(self, step_id, wait=False)

Kick off processing for the step with the provided id.

Parameters:

Name	Type	Description	Default
step_id	str	the id of the step that should be started	required

Source code in kiara/models/module/pipeline/controller.py

def process_step(self, step_id: str, wait: bool = False) -> uuid.UUID:
    """Kick off processing for the step with the provided id.

    Arguments:
        step_id: the id of the step that should be started
    """

    job_config = self.pipeline.create_job_config_for_step(step_id)

    job_id = self._job_registry.execute_job(job_config=job_config)
    # job_id = self._processor.create_job(job_config=job_config)
    # self._processor.queue_job(job_id=job_id)

    if wait:
        self._job_registry.wait_for(job_id)

    return job_id

set_processing_results(self, job_ids)

Source code in kiara/models/module/pipeline/controller.py

def set_processing_results(self, job_ids: Mapping[str, uuid.UUID]):

    self._job_registry.wait_for(*job_ids.values())

    combined_outputs = {}
    for step_id, job_id in job_ids.items():
        record = self._job_registry.get_job_record_in_session(job_id=job_id)
        combined_outputs[step_id] = record.outputs

    self.pipeline.set_multiple_step_outputs(
        changed_outputs=combined_outputs, notify_listeners=True
    )

pipeline

Classes

Pipeline

An instance of a [PipelineStructure][kiara.pipeline.structure.PipelineStructure] that holds state for all of the inputs/outputs of the steps within.

Source code in kiara/models/module/pipeline/pipeline.py

class Pipeline(object):
    """An instance of a [PipelineStructure][kiara.pipeline.structure.PipelineStructure] that holds state for all of the inputs/outputs of the steps within."""

    def __init__(self, structure: PipelineStructure, data_registry: DataRegistry):

        self._id: uuid.UUID = uuid.uuid4()

        self._structure: PipelineStructure = structure

        self._value_refs: Mapping[AliasValueMap, Iterable[ValueRef]] = None  # type: ignore
        # self._status: StepStatus = StepStatus.STALE

        self._steps_by_stage: Dict[int, Dict[str, PipelineStep]] = None  # type: ignore
        self._inputs_by_stage: Dict[int, List[str]] = None  # type: ignore
        self._outputs_by_stage: Dict[int, List[str]] = None  # type: ignore

        self._data_registry: DataRegistry = data_registry

        self._all_values: AliasValueMap = None  # type: ignore

        self._init_values()

        self._listeners: List[PipelineListener] = []

        # self._update_status()

    @property
    def pipeline_id(self) -> uuid.UUID:
        return self._id

    @property
    def kiara_id(self) -> uuid.UUID:
        return self._data_registry.kiara_id

    def _init_values(self):
        """Initialize this object. This should only be called once.

        Basically, this goes through all the inputs and outputs of all steps, and 'allocates' a PipelineValueInfo object
        for each of them. In case where output/input or pipeline-input/input points are connected, only one
        value item is allocated, since those refer to the same value.
        """

        values = AliasValueMap(
            alias=str(self.id), version=0, assoc_value=None, values_schema={}
        )
        values._data_registry = self._data_registry
        for field_name, schema in self._structure.pipeline_inputs_schema.items():
            values.set_alias_schema(f"pipeline.inputs.{field_name}", schema=schema)
        for field_name, schema in self._structure.pipeline_outputs_schema.items():
            values.set_alias_schema(f"pipeline.outputs.{field_name}", schema=schema)
        for step_id in self.step_ids:
            step = self.get_step(step_id)
            for field_name, value_schema in step.module.inputs_schema.items():
                values.set_alias_schema(
                    f"steps.{step_id}.inputs.{field_name}", schema=value_schema
                )
            for field_name, value_schema in step.module.outputs_schema.items():
                values.set_alias_schema(
                    f"steps.{step_id}.outputs.{field_name}", schema=value_schema
                )

        self._all_values = values

    def __eq__(self, other):

        if not isinstance(other, Pipeline):
            return False

        return self._id == other._id

    def __hash__(self):

        return hash(self._id)

    def add_listener(self, listener: PipelineListener):

        self._listeners.append(listener)

    @property
    def id(self) -> uuid.UUID:
        return self._id

    @property
    def structure(self) -> PipelineStructure:
        return self._structure

    def get_current_pipeline_inputs(self) -> Dict[str, uuid.UUID]:
        """All (pipeline) input values of this pipeline."""

        alias_map = self._all_values.get_alias("pipeline.inputs")
        return alias_map.get_all_value_ids()  # type: ignore

    def get_current_pipeline_outputs(self) -> Dict[str, uuid.UUID]:
        """All (pipeline) output values of this pipeline."""

        alias_map = self._all_values.get_alias("pipeline.outputs")
        return alias_map.get_all_value_ids()  # type: ignore

    def get_current_step_inputs(self, step_id) -> Dict[str, uuid.UUID]:

        alias_map = self._all_values.get_alias(f"steps.{step_id}.inputs")
        return alias_map.get_all_value_ids()  # type: ignore

    def get_current_step_outputs(self, step_id) -> Dict[str, uuid.UUID]:

        alias_map = self._all_values.get_alias(f"steps.{step_id}.outputs")
        return alias_map.get_all_value_ids()  # type: ignore

    def get_inputs_for_steps(self, *step_ids: str) -> Dict[str, Dict[str, uuid.UUID]]:
        """Retrieve value ids for the inputs of the specified steps (or all steps, if no argument provided."""

        result = {}
        for step_id in self._structure.step_ids:
            if step_ids and step_id not in step_ids:
                continue
            ids = self.get_current_step_inputs(step_id=step_id)
            result[step_id] = ids
        return result

    def get_outputs_for_steps(self, *step_ids: str) -> Dict[str, Dict[str, uuid.UUID]]:
        """Retrieve value ids for the outputs of the specified steps (or all steps, if no argument provided."""

        result = {}
        for step_id in self._structure.step_ids:
            if step_ids and step_id not in step_ids:
                continue
            ids = self.get_current_step_outputs(step_id=step_id)
            result[step_id] = ids
        return result

    def _notify_pipeline_listeners(self, event: PipelineEvent):

        for listener in self._listeners:
            listener._pipeline_event_occurred(event=event)

    def get_pipeline_details(self) -> PipelineDetails:

        pipeline_inputs = self._all_values.get_alias("pipeline.inputs")
        pipeline_outputs = self._all_values.get_alias("pipeline.outputs")
        assert pipeline_inputs is not None
        assert pipeline_outputs is not None

        invalid = pipeline_inputs.check_invalid()
        if not invalid:
            status = StepStatus.INPUTS_READY
            step_outputs = self._all_values.get_alias("pipeline.outputs")
            assert step_outputs is not None
            invalid_outputs = step_outputs.check_invalid()
            # TODO: also check that all the pedigrees match up with current inputs
            if not invalid_outputs:
                status = StepStatus.RESULTS_READY
        else:
            status = StepStatus.INPUTS_INVALID

        step_states = {}
        for step_id in self._structure.step_ids:
            d = self.get_step_details(step_id)
            step_states[step_id] = d

        details = PipelineDetails.construct(
            kiara_id=self._data_registry.kiara_id,
            pipeline_id=self.pipeline_id,
            pipeline_status=status,
            pipeline_inputs=pipeline_inputs.get_all_value_ids(),
            pipeline_outputs=pipeline_outputs.get_all_value_ids(),
            invalid_details=invalid,
            step_states=step_states,
        )

        return details

    def get_step_details(self, step_id: str) -> StepDetails:

        step_input_ids = self.get_current_step_inputs(step_id=step_id)
        step_output_ids = self.get_current_step_outputs(step_id=step_id)
        step_inputs = self._all_values.get_alias(f"steps.{step_id}.inputs")

        assert step_inputs is not None
        invalid = step_inputs.check_invalid()

        processing_stage = self._structure.get_processing_stage(step_id)

        if not invalid:
            status = StepStatus.INPUTS_READY
            step_outputs = self._all_values.get_alias(f"steps.{step_id}.outputs")
            assert step_outputs is not None
            invalid_outputs = step_outputs.check_invalid()
            # TODO: also check that all the pedigrees match up with current inputs
            if not invalid_outputs:
                status = StepStatus.RESULTS_READY
        else:
            status = StepStatus.INPUTS_INVALID

        details = StepDetails.construct(
            kiara_id=self._data_registry.kiara_id,
            pipeline_id=self.pipeline_id,
            step_id=step_id,
            status=status,
            inputs=step_input_ids,
            outputs=step_output_ids,
            invalid_details=invalid,
            processing_stage=processing_stage,
        )
        return details

    def set_pipeline_inputs(
        self,
        inputs: Mapping[str, Any],
        sync_to_step_inputs: bool = True,
        notify_listeners: bool = True,
    ) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

        values_to_set: Dict[str, uuid.UUID] = {}

        for k, v in inputs.items():
            if v is None:
                values_to_set[k] = NONE_VALUE_ID
            else:
                alias_map = self._all_values.get_alias("pipeline.inputs")
                assert alias_map is not None
                # dbg(alias_map.__dict__)
                schema = alias_map.values_schema[k]
                value = self._data_registry.register_data(
                    data=v, schema=schema, pedigree=ORPHAN, reuse_existing=True
                )
                values_to_set[k] = value.value_id

        changed_pipeline_inputs = self._set_values("pipeline.inputs", **values_to_set)

        changed_results = {"__pipeline__": {"inputs": changed_pipeline_inputs}}

        if sync_to_step_inputs:
            changed = self.sync_pipeline_inputs(notify_listeners=False)
            dpath.util.merge(changed_results, changed)  # type: ignore

        if notify_listeners:
            event = PipelineEvent.create_event(pipeline=self, changed=changed_results)
            self._notify_pipeline_listeners(event)

        return changed_results

    def sync_pipeline_inputs(
        self, notify_listeners: bool = True
    ) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

        pipeline_inputs = self.get_current_pipeline_inputs()

        values_to_sync: Dict[str, Dict[str, Optional[uuid.UUID]]] = {}

        for field_name, ref in self._structure.pipeline_input_refs.items():
            for step_input in ref.connected_inputs:
                step_inputs = self.get_current_step_inputs(step_input.step_id)

                if step_inputs[step_input.value_name] != pipeline_inputs[field_name]:
                    values_to_sync.setdefault(step_input.step_id, {})[
                        step_input.value_name
                    ] = pipeline_inputs[field_name]

        results: Dict[str, Mapping[str, Mapping[str, ChangedValue]]] = {}
        for step_id in values_to_sync.keys():
            values = values_to_sync[step_id]
            step_changed = self._set_step_inputs(step_id=step_id, inputs=values)
            dpath.util.merge(results, step_changed)  # type: ignore

        if notify_listeners:
            event = PipelineEvent.create_event(pipeline=self, changed=results)
            self._notify_pipeline_listeners(event)

        return results

    def _set_step_inputs(
        self, step_id: str, inputs: Mapping[str, Optional[uuid.UUID]]
    ) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

        changed_step_inputs = self._set_values(f"steps.{step_id}.inputs", **inputs)
        if not changed_step_inputs:
            return {}

        result: Dict[str, Dict[str, Dict[str, ChangedValue]]] = {
            step_id: {"inputs": changed_step_inputs}
        }

        step_outputs = self._structure.get_step_output_refs(step_id=step_id)
        null_outputs = {k: None for k in step_outputs.keys()}

        changed_outputs = self.set_step_outputs(
            step_id=step_id, outputs=null_outputs, notify_listeners=False
        )
        assert step_id not in changed_outputs.keys()

        result.update(changed_outputs)  # type: ignore

        return result

    def set_multiple_step_outputs(
        self,
        changed_outputs: Mapping[str, Mapping[str, Optional[uuid.UUID]]],
        notify_listeners: bool = True,
    ) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

        results: Dict[str, Dict[str, Dict[str, ChangedValue]]] = {}
        for step_id, outputs in changed_outputs.items():
            step_results = self.set_step_outputs(
                step_id=step_id, outputs=outputs, notify_listeners=False
            )
            dpath.util.merge(results, step_results)  # type: ignore

        if notify_listeners:
            event = PipelineEvent.create_event(pipeline=self, changed=results)
            self._notify_pipeline_listeners(event)

        return results

    def set_step_outputs(
        self,
        step_id: str,
        outputs: Mapping[str, Optional[uuid.UUID]],
        notify_listeners: bool = True,
    ) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

        # make sure pedigrees match with respective inputs?

        changed_step_outputs = self._set_values(f"steps.{step_id}.outputs", **outputs)
        if not changed_step_outputs:
            return {}

        result: Dict[str, Dict[str, Dict[str, ChangedValue]]] = {
            step_id: {"outputs": changed_step_outputs}
        }

        output_refs = self._structure.get_step_output_refs(step_id=step_id)

        pipeline_outputs: Dict[str, Optional[uuid.UUID]] = {}

        inputs_to_set: Dict[str, Dict[str, Optional[uuid.UUID]]] = {}

        for field_name, ref in output_refs.items():
            if ref.pipeline_output:
                assert ref.pipeline_output not in pipeline_outputs.keys()
                pipeline_outputs[ref.pipeline_output] = outputs[field_name]
            for input_ref in ref.connected_inputs:
                inputs_to_set.setdefault(input_ref.step_id, {})[
                    input_ref.value_name
                ] = outputs[field_name]

        for step_id, step_inputs in inputs_to_set.items():
            changed_step_fields = self._set_step_inputs(
                step_id=step_id, inputs=step_inputs
            )
            dpath.util.merge(result, changed_step_fields)  # type: ignore

        if pipeline_outputs:
            changed_pipeline_outputs = self._set_pipeline_outputs(**pipeline_outputs)
            dpath.util.merge(  # type: ignore
                result, {"__pipeline__": {"outputs": changed_pipeline_outputs}}
            )

        if notify_listeners:
            event = PipelineEvent.create_event(pipeline=self, changed=result)
            self._notify_pipeline_listeners(event)

        return result

    def _set_pipeline_outputs(
        self, **outputs: Optional[uuid.UUID]
    ) -> Mapping[str, ChangedValue]:

        changed_pipeline_outputs = self._set_values("pipeline.outputs", **outputs)
        return changed_pipeline_outputs

    def _set_values(
        self, alias: str, **values: Optional[uuid.UUID]
    ) -> Dict[str, ChangedValue]:
        """Set values (value-ids) for the sub-alias-map with the specified alias path."""

        invalid = {}
        for k in values.keys():
            _alias = self._all_values.get_alias(alias)
            assert _alias is not None
            if k not in _alias.values_schema.keys():
                invalid[
                    k
                ] = f"Invalid field '{k}'. Available fields: {', '.join(self.get_current_pipeline_inputs().keys())}"

        if invalid:
            raise InvalidValuesException(invalid_values=invalid)

        alias_map: Optional[AliasValueMap] = self._all_values.get_alias(alias)
        assert alias_map is not None

        values_to_set: Dict[str, Optional[uuid.UUID]] = {}
        current: Dict[str, Optional[uuid.UUID]] = {}
        changed: Dict[str, ChangedValue] = {}

        for field_name, new_value in values.items():

            current_value = self._all_values.get_alias(f"{alias}.{field_name}")
            if current_value is not None:
                current_value_id = current_value.assoc_value
            else:
                current_value_id = None
            current[field_name] = current_value_id

            if current_value_id != new_value:
                values_to_set[field_name] = new_value
                changed[field_name] = ChangedValue(old=current_value_id, new=new_value)

        _alias = self._all_values.get_alias(alias)
        assert _alias is not None
        _alias.set_aliases(**values_to_set)

        return changed

    @property
    def step_ids(self) -> Iterable[str]:
        """Return all ids of the steps of this pipeline."""
        return self._structure.step_ids

    def get_step(self, step_id: str) -> PipelineStep:
        """Return the object representing a step in this workflow, identified by the step id."""
        return self._structure.get_step(step_id)

    def get_steps_by_stage(
        self,
    ) -> Mapping[int, Mapping[str, PipelineStep]]:
        """Return a all pipeline steps, ordered by stage they belong to."""

        if self._steps_by_stage is not None:
            return self._steps_by_stage

        result: Dict[int, Dict[str, PipelineStep]] = {}
        for step_id in self.step_ids:
            step = self.get_step(step_id)
            stage = self._structure.get_processing_stage(step.step_id)
            assert stage is not None
            result.setdefault(stage, {})[step_id] = step

        self._steps_by_stage = result
        return self._steps_by_stage

    def create_job_config_for_step(self, step_id: str) -> JobConfig:

        step_inputs: Mapping[str, Optional[uuid.UUID]] = self.get_current_step_inputs(
            step_id
        )
        step_details: StepDetails = self.get_step_details(step_id=step_id)
        step: PipelineStep = self.get_step(step_id=step_id)

        # if the inputs are not valid, ignore this step
        if step_details.status == StepStatus.INPUTS_INVALID:
            invalid_details = step_details.invalid_details
            assert invalid_details is not None
            msg = f"Can't execute step '{step_id}', invalid inputs: {', '.join(invalid_details.keys())}"
            raise InvalidValuesException(msg=msg, invalid_values=invalid_details)

        job_config = JobConfig.create_from_module(
            data_registry=self._data_registry, module=step.module, inputs=step_inputs
        )
        return job_config

Attributes

id: UUID property readonly

kiara_id: UUID property readonly

pipeline_id: UUID property readonly

step_ids: Iterable[str] property readonly

Return all ids of the steps of this pipeline.

structure: PipelineStructure property readonly

Methods

add_listener(self, listener)

Source code in kiara/models/module/pipeline/pipeline.py

def add_listener(self, listener: PipelineListener):

    self._listeners.append(listener)

create_job_config_for_step(self, step_id)

Source code in kiara/models/module/pipeline/pipeline.py

def create_job_config_for_step(self, step_id: str) -> JobConfig:

    step_inputs: Mapping[str, Optional[uuid.UUID]] = self.get_current_step_inputs(
        step_id
    )
    step_details: StepDetails = self.get_step_details(step_id=step_id)
    step: PipelineStep = self.get_step(step_id=step_id)

    # if the inputs are not valid, ignore this step
    if step_details.status == StepStatus.INPUTS_INVALID:
        invalid_details = step_details.invalid_details
        assert invalid_details is not None
        msg = f"Can't execute step '{step_id}', invalid inputs: {', '.join(invalid_details.keys())}"
        raise InvalidValuesException(msg=msg, invalid_values=invalid_details)

    job_config = JobConfig.create_from_module(
        data_registry=self._data_registry, module=step.module, inputs=step_inputs
    )
    return job_config

get_current_pipeline_inputs(self)

All (pipeline) input values of this pipeline.

Source code in kiara/models/module/pipeline/pipeline.py

def get_current_pipeline_inputs(self) -> Dict[str, uuid.UUID]:
    """All (pipeline) input values of this pipeline."""

    alias_map = self._all_values.get_alias("pipeline.inputs")
    return alias_map.get_all_value_ids()  # type: ignore

get_current_pipeline_outputs(self)

All (pipeline) output values of this pipeline.

Source code in kiara/models/module/pipeline/pipeline.py

def get_current_pipeline_outputs(self) -> Dict[str, uuid.UUID]:
    """All (pipeline) output values of this pipeline."""

    alias_map = self._all_values.get_alias("pipeline.outputs")
    return alias_map.get_all_value_ids()  # type: ignore

get_current_step_inputs(self, step_id)

Source code in kiara/models/module/pipeline/pipeline.py

def get_current_step_inputs(self, step_id) -> Dict[str, uuid.UUID]:

    alias_map = self._all_values.get_alias(f"steps.{step_id}.inputs")
    return alias_map.get_all_value_ids()  # type: ignore

get_current_step_outputs(self, step_id)

Source code in kiara/models/module/pipeline/pipeline.py

def get_current_step_outputs(self, step_id) -> Dict[str, uuid.UUID]:

    alias_map = self._all_values.get_alias(f"steps.{step_id}.outputs")
    return alias_map.get_all_value_ids()  # type: ignore

get_inputs_for_steps(self, *step_ids)

Retrieve value ids for the inputs of the specified steps (or all steps, if no argument provided.

Source code in kiara/models/module/pipeline/pipeline.py

def get_inputs_for_steps(self, *step_ids: str) -> Dict[str, Dict[str, uuid.UUID]]:
    """Retrieve value ids for the inputs of the specified steps (or all steps, if no argument provided."""

    result = {}
    for step_id in self._structure.step_ids:
        if step_ids and step_id not in step_ids:
            continue
        ids = self.get_current_step_inputs(step_id=step_id)
        result[step_id] = ids
    return result

get_outputs_for_steps(self, *step_ids)

Retrieve value ids for the outputs of the specified steps (or all steps, if no argument provided.

Source code in kiara/models/module/pipeline/pipeline.py

def get_outputs_for_steps(self, *step_ids: str) -> Dict[str, Dict[str, uuid.UUID]]:
    """Retrieve value ids for the outputs of the specified steps (or all steps, if no argument provided."""

    result = {}
    for step_id in self._structure.step_ids:
        if step_ids and step_id not in step_ids:
            continue
        ids = self.get_current_step_outputs(step_id=step_id)
        result[step_id] = ids
    return result

get_pipeline_details(self)

Source code in kiara/models/module/pipeline/pipeline.py

def get_pipeline_details(self) -> PipelineDetails:

    pipeline_inputs = self._all_values.get_alias("pipeline.inputs")
    pipeline_outputs = self._all_values.get_alias("pipeline.outputs")
    assert pipeline_inputs is not None
    assert pipeline_outputs is not None

    invalid = pipeline_inputs.check_invalid()
    if not invalid:
        status = StepStatus.INPUTS_READY
        step_outputs = self._all_values.get_alias("pipeline.outputs")
        assert step_outputs is not None
        invalid_outputs = step_outputs.check_invalid()
        # TODO: also check that all the pedigrees match up with current inputs
        if not invalid_outputs:
            status = StepStatus.RESULTS_READY
    else:
        status = StepStatus.INPUTS_INVALID

    step_states = {}
    for step_id in self._structure.step_ids:
        d = self.get_step_details(step_id)
        step_states[step_id] = d

    details = PipelineDetails.construct(
        kiara_id=self._data_registry.kiara_id,
        pipeline_id=self.pipeline_id,
        pipeline_status=status,
        pipeline_inputs=pipeline_inputs.get_all_value_ids(),
        pipeline_outputs=pipeline_outputs.get_all_value_ids(),
        invalid_details=invalid,
        step_states=step_states,
    )

    return details

get_step(self, step_id)

Return the object representing a step in this workflow, identified by the step id.

Source code in kiara/models/module/pipeline/pipeline.py

def get_step(self, step_id: str) -> PipelineStep:
    """Return the object representing a step in this workflow, identified by the step id."""
    return self._structure.get_step(step_id)

get_step_details(self, step_id)

Source code in kiara/models/module/pipeline/pipeline.py

def get_step_details(self, step_id: str) -> StepDetails:

    step_input_ids = self.get_current_step_inputs(step_id=step_id)
    step_output_ids = self.get_current_step_outputs(step_id=step_id)
    step_inputs = self._all_values.get_alias(f"steps.{step_id}.inputs")

    assert step_inputs is not None
    invalid = step_inputs.check_invalid()

    processing_stage = self._structure.get_processing_stage(step_id)

    if not invalid:
        status = StepStatus.INPUTS_READY
        step_outputs = self._all_values.get_alias(f"steps.{step_id}.outputs")
        assert step_outputs is not None
        invalid_outputs = step_outputs.check_invalid()
        # TODO: also check that all the pedigrees match up with current inputs
        if not invalid_outputs:
            status = StepStatus.RESULTS_READY
    else:
        status = StepStatus.INPUTS_INVALID

    details = StepDetails.construct(
        kiara_id=self._data_registry.kiara_id,
        pipeline_id=self.pipeline_id,
        step_id=step_id,
        status=status,
        inputs=step_input_ids,
        outputs=step_output_ids,
        invalid_details=invalid,
        processing_stage=processing_stage,
    )
    return details

get_steps_by_stage(self)

Return a all pipeline steps, ordered by stage they belong to.

Source code in kiara/models/module/pipeline/pipeline.py

def get_steps_by_stage(
    self,
) -> Mapping[int, Mapping[str, PipelineStep]]:
    """Return a all pipeline steps, ordered by stage they belong to."""

    if self._steps_by_stage is not None:
        return self._steps_by_stage

    result: Dict[int, Dict[str, PipelineStep]] = {}
    for step_id in self.step_ids:
        step = self.get_step(step_id)
        stage = self._structure.get_processing_stage(step.step_id)
        assert stage is not None
        result.setdefault(stage, {})[step_id] = step

    self._steps_by_stage = result
    return self._steps_by_stage

set_multiple_step_outputs(self, changed_outputs, notify_listeners=True)

Source code in kiara/models/module/pipeline/pipeline.py

def set_multiple_step_outputs(
    self,
    changed_outputs: Mapping[str, Mapping[str, Optional[uuid.UUID]]],
    notify_listeners: bool = True,
) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

    results: Dict[str, Dict[str, Dict[str, ChangedValue]]] = {}
    for step_id, outputs in changed_outputs.items():
        step_results = self.set_step_outputs(
            step_id=step_id, outputs=outputs, notify_listeners=False
        )
        dpath.util.merge(results, step_results)  # type: ignore

    if notify_listeners:
        event = PipelineEvent.create_event(pipeline=self, changed=results)
        self._notify_pipeline_listeners(event)

    return results

set_pipeline_inputs(self, inputs, sync_to_step_inputs=True, notify_listeners=True)

Source code in kiara/models/module/pipeline/pipeline.py

def set_pipeline_inputs(
    self,
    inputs: Mapping[str, Any],
    sync_to_step_inputs: bool = True,
    notify_listeners: bool = True,
) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

    values_to_set: Dict[str, uuid.UUID] = {}

    for k, v in inputs.items():
        if v is None:
            values_to_set[k] = NONE_VALUE_ID
        else:
            alias_map = self._all_values.get_alias("pipeline.inputs")
            assert alias_map is not None
            # dbg(alias_map.__dict__)
            schema = alias_map.values_schema[k]
            value = self._data_registry.register_data(
                data=v, schema=schema, pedigree=ORPHAN, reuse_existing=True
            )
            values_to_set[k] = value.value_id

    changed_pipeline_inputs = self._set_values("pipeline.inputs", **values_to_set)

    changed_results = {"__pipeline__": {"inputs": changed_pipeline_inputs}}

    if sync_to_step_inputs:
        changed = self.sync_pipeline_inputs(notify_listeners=False)
        dpath.util.merge(changed_results, changed)  # type: ignore

    if notify_listeners:
        event = PipelineEvent.create_event(pipeline=self, changed=changed_results)
        self._notify_pipeline_listeners(event)

    return changed_results

set_step_outputs(self, step_id, outputs, notify_listeners=True)

Source code in kiara/models/module/pipeline/pipeline.py

def set_step_outputs(
    self,
    step_id: str,
    outputs: Mapping[str, Optional[uuid.UUID]],
    notify_listeners: bool = True,
) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

    # make sure pedigrees match with respective inputs?

    changed_step_outputs = self._set_values(f"steps.{step_id}.outputs", **outputs)
    if not changed_step_outputs:
        return {}

    result: Dict[str, Dict[str, Dict[str, ChangedValue]]] = {
        step_id: {"outputs": changed_step_outputs}
    }

    output_refs = self._structure.get_step_output_refs(step_id=step_id)

    pipeline_outputs: Dict[str, Optional[uuid.UUID]] = {}

    inputs_to_set: Dict[str, Dict[str, Optional[uuid.UUID]]] = {}

    for field_name, ref in output_refs.items():
        if ref.pipeline_output:
            assert ref.pipeline_output not in pipeline_outputs.keys()
            pipeline_outputs[ref.pipeline_output] = outputs[field_name]
        for input_ref in ref.connected_inputs:
            inputs_to_set.setdefault(input_ref.step_id, {})[
                input_ref.value_name
            ] = outputs[field_name]

    for step_id, step_inputs in inputs_to_set.items():
        changed_step_fields = self._set_step_inputs(
            step_id=step_id, inputs=step_inputs
        )
        dpath.util.merge(result, changed_step_fields)  # type: ignore

    if pipeline_outputs:
        changed_pipeline_outputs = self._set_pipeline_outputs(**pipeline_outputs)
        dpath.util.merge(  # type: ignore
            result, {"__pipeline__": {"outputs": changed_pipeline_outputs}}
        )

    if notify_listeners:
        event = PipelineEvent.create_event(pipeline=self, changed=result)
        self._notify_pipeline_listeners(event)

    return result

sync_pipeline_inputs(self, notify_listeners=True)

Source code in kiara/models/module/pipeline/pipeline.py

def sync_pipeline_inputs(
    self, notify_listeners: bool = True
) -> Mapping[str, Mapping[str, Mapping[str, ChangedValue]]]:

    pipeline_inputs = self.get_current_pipeline_inputs()

    values_to_sync: Dict[str, Dict[str, Optional[uuid.UUID]]] = {}

    for field_name, ref in self._structure.pipeline_input_refs.items():
        for step_input in ref.connected_inputs:
            step_inputs = self.get_current_step_inputs(step_input.step_id)

            if step_inputs[step_input.value_name] != pipeline_inputs[field_name]:
                values_to_sync.setdefault(step_input.step_id, {})[
                    step_input.value_name
                ] = pipeline_inputs[field_name]

    results: Dict[str, Mapping[str, Mapping[str, ChangedValue]]] = {}
    for step_id in values_to_sync.keys():
        values = values_to_sync[step_id]
        step_changed = self._set_step_inputs(step_id=step_id, inputs=values)
        dpath.util.merge(results, step_changed)  # type: ignore

    if notify_listeners:
        event = PipelineEvent.create_event(pipeline=self, changed=results)
        self._notify_pipeline_listeners(event)

    return results

PipelineListener (ABC)

Source code in kiara/models/module/pipeline/pipeline.py

class PipelineListener(abc.ABC):
    @abc.abstractmethod
    def _pipeline_event_occurred(self, event: PipelineEvent):
        pass

structure

Classes

PipelineStructure (KiaraModel) pydantic-model

An object that holds one or several steps, and describes the connections between them.

Source code in kiara/models/module/pipeline/structure.py

class PipelineStructure(KiaraModel):
    """An object that holds one or several steps, and describes the connections between them."""

    _kiara_model_id = "instance.pipeline_structure"

    pipeline_config: PipelineConfig = Field(
        description="The underlying pipeline config."
    )
    steps: List[PipelineStep] = Field(description="The pipeline steps ")
    input_aliases: Dict[str, str] = Field(description="The input aliases.")
    output_aliases: Dict[str, str] = Field(description="The output aliases.")

    @root_validator(pre=True)
    def validate_pipeline_config(cls, values):

        pipeline_config = values.get("pipeline_config", None)
        if not pipeline_config:
            raise ValueError("No 'pipeline_config' provided.")

        if len(values) != 1:
            raise ValueError(
                "Only 'pipeline_config' key allowed when creating a pipeline structure object."
            )

        _config: PipelineConfig = pipeline_config
        _steps: List[PipelineStep] = list(_config.steps)

        _input_aliases: Dict[str, str] = dict(_config.input_aliases)
        _output_aliases: Dict[str, str] = dict(_config.output_aliases)

        invalid_input_aliases = [a for a in _input_aliases.values() if "." in a]
        if invalid_input_aliases:
            raise Exception(
                f"Invalid input aliases, aliases can't contain special characters: {', '.join(invalid_input_aliases)}."
            )
        invalid_output_aliases = [a for a in _input_aliases.values() if "." in a]
        if invalid_input_aliases:
            raise Exception(
                f"Invalid input aliases, aliases can't contain special characters: {', '.join(invalid_output_aliases)}."
            )

        valid_input_names = set()
        for step in _steps:
            for input_name in step.module.input_names:
                valid_input_names.add(f"{step.step_id}.{input_name}")
        invalid_input_aliases = [
            a for a in _input_aliases.keys() if a not in valid_input_names
        ]
        if invalid_input_aliases:
            raise Exception(
                f"Invalid input reference(s): {', '.join(invalid_input_aliases)}. Must be one of: {', '.join(valid_input_names)}"
            )
        valid_output_names = set()
        for step in _steps:
            for output_name in step.module.output_names:
                valid_output_names.add(f"{step.step_id}.{output_name}")
        invalid_output_names = [
            a for a in _output_aliases.keys() if a not in valid_output_names
        ]
        if invalid_output_names:
            raise Exception(
                f"Invalid output reference(s): {', '.join(invalid_output_names)}. Must be one of: {', '.join(valid_output_names)}"
            )

        values["steps"] = _steps
        values["input_aliases"] = _input_aliases
        values["output_aliases"] = _output_aliases
        return values

    # this is hardcoded for now
    _add_all_workflow_outputs: bool = PrivateAttr(default=False)
    _constants: Dict[str, Any] = PrivateAttr(default=None)  # type: ignore
    _defaults: Dict[str, Any] = PrivateAttr(None)  # type: ignore

    _execution_graph: nx.DiGraph = PrivateAttr(None)  # type: ignore
    _data_flow_graph: nx.DiGraph = PrivateAttr(None)  # type: ignore
    _data_flow_graph_simple: nx.DiGraph = PrivateAttr(None)  # type: ignore

    _processing_stages: List[List[str]] = PrivateAttr(None)  # type: ignore

    # holds details about the (current) processing steps contained in this workflow
    _steps_details: Dict[str, Any] = PrivateAttr(None)  # type: ignore

    def _retrieve_data_to_hash(self) -> Any:
        return {
            "steps": [step.instance_cid for step in self.steps],
            "input_aliases": self.input_aliases,
            "output_aliases": self.output_aliases,
        }

    def _retrieve_id(self) -> str:
        return self.pipeline_config.instance_id

    @property
    def steps_details(self) -> Mapping[str, Any]:

        if self._steps_details is None:
            self._process_steps()
        return self._steps_details  # type: ignore

    @property
    def step_ids(self) -> Iterable[str]:
        if self._steps_details is None:
            self._process_steps()
        return self._steps_details.keys()  # type: ignore

    @property
    def constants(self) -> Mapping[str, Any]:

        if self._constants is None:
            self._process_steps()
        return self._constants  # type: ignore

    @property
    def defaults(self) -> Mapping[str, Any]:

        if self._defaults is None:
            self._process_steps()
        return self._defaults  # type: ignore

    def get_step(self, step_id: str) -> PipelineStep:

        d = self.steps_details.get(step_id, None)
        if d is None:
            raise Exception(f"No module with id: {step_id}")

        return d["step"]

    def get_step_input_refs(self, step_id: str) -> Mapping[str, StepInputRef]:

        d = self.steps_details.get(step_id, None)
        if d is None:
            raise Exception(f"No module with id: {step_id}")

        return d["inputs"]

    def get_step_output_refs(self, step_id: str) -> Mapping[str, StepOutputRef]:

        d = self.steps_details.get(step_id, None)
        if d is None:
            raise Exception(f"No module with id: {step_id}")

        return d["outputs"]

    def get_step_details(self, step_id: str) -> Mapping[str, Any]:

        d = self.steps_details.get(step_id, None)
        if d is None:
            raise Exception(f"No module with id: {step_id}")

        return d

    @property
    def execution_graph(self) -> nx.DiGraph:
        if self._execution_graph is None:
            self._process_steps()
        return self._execution_graph

    @property
    def data_flow_graph(self) -> nx.DiGraph:
        if self._data_flow_graph is None:
            self._process_steps()
        return self._data_flow_graph

    @property
    def data_flow_graph_simple(self) -> nx.DiGraph:
        if self._data_flow_graph_simple is None:
            self._process_steps()
        return self._data_flow_graph_simple

    @property
    def processing_stages(self) -> List[List[str]]:
        if self._steps_details is None:
            self._process_steps()
        return self._processing_stages

    @lru_cache()
    def _get_node_of_type(self, node_type: str):
        if self._steps_details is None:
            self._process_steps()

        return [
            node
            for node, attr in self._data_flow_graph.nodes(data=True)
            if attr["type"] == node_type
        ]

    @property
    def steps_input_refs(self) -> Dict[str, StepInputRef]:
        return {
            node.alias: node
            for node in self._get_node_of_type(node_type=StepInputRef.__name__)
        }

    @property
    def steps_output_refs(self) -> Dict[str, StepOutputRef]:
        return {
            node.alias: node
            for node in self._get_node_of_type(node_type=StepOutputRef.__name__)
        }

    @property
    def pipeline_input_refs(self) -> Dict[str, PipelineInputRef]:
        return {
            node.value_name: node
            for node in self._get_node_of_type(node_type=PipelineInputRef.__name__)
        }

    @property
    def pipeline_output_refs(self) -> Dict[str, PipelineOutputRef]:
        return {
            node.value_name: node
            for node in self._get_node_of_type(node_type=PipelineOutputRef.__name__)
        }

    @property
    def pipeline_inputs_schema(self) -> Mapping[str, ValueSchema]:

        schemas = {
            input_name: w_in.value_schema
            for input_name, w_in in self.pipeline_input_refs.items()
        }
        return schemas

    @property
    def pipeline_outputs_schema(self) -> Mapping[str, ValueSchema]:
        return {
            output_name: w_out.value_schema
            for output_name, w_out in self.pipeline_output_refs.items()
        }

    def get_processing_stage(self, step_id: str) -> int:
        """Return the processing stage for the specified step_id.

        Returns the stage nr (starting with '1').
        """

        for index, stage in enumerate(self.processing_stages, start=1):
            if step_id in stage:
                return index

        raise Exception(f"Invalid step id '{step_id}'.")

    def step_is_required(self, step_id: str) -> bool:
        """Check if the specified step is required, or can be omitted."""

        return self.get_step_details(step_id=step_id)["required"]

    def _process_steps(self):
        """The core method of this class, it connects all the processing modules, their inputs and outputs."""

        steps_details: Dict[str, Any] = {}
        execution_graph = nx.DiGraph()
        execution_graph.add_node("__root__")
        data_flow_graph = nx.DiGraph()
        data_flow_graph_simple = nx.DiGraph()
        processing_stages = []
        constants = {}
        structure_defaults = {}

        # temp variable, to hold all outputs
        outputs: Dict[str, StepOutputRef] = {}

        # process all pipeline and step outputs first
        _temp_steps_map: Dict[str, PipelineStep] = {}
        pipeline_outputs: Dict[str, PipelineOutputRef] = {}
        for step in self.steps:

            _temp_steps_map[step.step_id] = step

            if step.step_id in steps_details.keys():
                raise Exception(
                    f"Can't process steps: duplicate step_id '{step.step_id}'"
                )

            steps_details[step.step_id] = {
                "step": step,
                "outputs": {},
                "inputs": {},
                "required": True,
            }

            data_flow_graph.add_node(step, type="step")

            # go through all the module outputs, create points for them and connect them to pipeline outputs
            for output_name, schema in step.module.outputs_schema.items():

                step_output = StepOutputRef(
                    value_name=output_name,
                    value_schema=schema,
                    step_id=step.step_id,
                )

                steps_details[step.step_id]["outputs"][output_name] = step_output
                step_alias = generate_step_alias(step.step_id, output_name)
                outputs[step_alias] = step_output

                # step_output_name = generate_pipeline_endpoint_name(
                #     step_id=step.step_id, value_name=output_name
                # )
                step_output_name = f"{step.step_id}.{output_name}"
                if not self.output_aliases:
                    raise NotImplementedError()
                if step_output_name in self.output_aliases.keys():
                    step_output_name = self.output_aliases[step_output_name]
                else:
                    if not self._add_all_workflow_outputs:
                        # this output is not interesting for the workflow
                        step_output_name = None

                if step_output_name:
                    step_output_address = StepValueAddress(
                        step_id=step.step_id, value_name=output_name
                    )
                    pipeline_output = PipelineOutputRef(
                        value_name=step_output_name,
                        connected_output=step_output_address,
                        value_schema=schema,
                    )
                    pipeline_outputs[step_output_name] = pipeline_output
                    step_output.pipeline_output = pipeline_output.value_name

                    data_flow_graph.add_node(
                        pipeline_output, type=PipelineOutputRef.__name__
                    )
                    data_flow_graph.add_edge(step_output, pipeline_output)

                    data_flow_graph_simple.add_node(
                        pipeline_output, type=PipelineOutputRef.__name__
                    )
                    data_flow_graph_simple.add_edge(step, pipeline_output)

                data_flow_graph.add_node(step_output, type=StepOutputRef.__name__)
                data_flow_graph.add_edge(step, step_output)

        # now process inputs, and connect them to the appropriate output/pipeline-input points
        existing_pipeline_input_points: Dict[str, PipelineInputRef] = {}
        for step in self.steps:

            other_step_dependency: Set = set()
            # go through all the inputs of a module, create input points and connect them to either
            # other module outputs, or pipeline inputs (which need to be created)

            module_constants: Mapping[str, Any] = step.module.get_config_value(
                "constants"
            )

            for input_name, schema in step.module.inputs_schema.items():

                matching_input_links: List[StepValueAddress] = []
                is_constant = input_name in module_constants.keys()

                for value_name, input_links in step.input_links.items():
                    if value_name == input_name:
                        for input_link in input_links:
                            if input_link in matching_input_links:
                                raise Exception(f"Duplicate input link: {input_link}")
                            matching_input_links.append(input_link)

                if matching_input_links:
                    # this means we connect to other steps output

                    connected_output_points: List[StepOutputRef] = []
                    connected_outputs: List[StepValueAddress] = []

                    for input_link in matching_input_links:
                        output_id = generate_step_alias(
                            input_link.step_id, input_link.value_name
                        )

                        if output_id not in outputs.keys():
                            raise Exception(
                                f"Can't connect input '{input_name}' for step '{step.step_id}': no output '{output_id}' available. Available output names: {', '.join(outputs.keys())}"
                            )
                        connected_output_points.append(outputs[output_id])
                        connected_outputs.append(input_link)

                        other_step_dependency.add(input_link.step_id)

                    step_input_point = StepInputRef(
                        step_id=step.step_id,
                        value_name=input_name,
                        value_schema=schema,
                        is_constant=is_constant,
                        connected_pipeline_input=None,
                        connected_outputs=connected_outputs,
                    )

                    for op in connected_output_points:
                        op.connected_inputs.append(step_input_point.address)
                        data_flow_graph.add_edge(op, step_input_point)
                        data_flow_graph_simple.add_edge(
                            _temp_steps_map[op.step_id], step_input_point
                        )  # TODO: name edge
                        data_flow_graph_simple.add_edge(
                            step_input_point, step
                        )  # TODO: name edge

                else:
                    # this means we connect to pipeline input
                    # pipeline_input_name = generate_pipeline_endpoint_name(
                    #     step_id=step.step_id, value_name=input_name
                    # )
                    pipeline_input_name = f"{step.step_id}.{input_name}"
                    # check whether this input has an alias associated with it
                    if not self.input_aliases:
                        raise NotImplementedError()

                    if pipeline_input_name in self.input_aliases.keys():
                        # this means we use the pipeline alias
                        pipeline_input_name = self.input_aliases[pipeline_input_name]

                    if pipeline_input_name in existing_pipeline_input_points.keys():
                        # we already created a pipeline input with this name
                        # TODO: check whether schema fits
                        connected_pipeline_input = existing_pipeline_input_points[
                            pipeline_input_name
                        ]
                        assert connected_pipeline_input.is_constant == is_constant
                    else:
                        # we need to create the pipeline input
                        connected_pipeline_input = PipelineInputRef(
                            value_name=pipeline_input_name,
                            value_schema=schema,
                            is_constant=is_constant,
                        )

                        existing_pipeline_input_points[
                            pipeline_input_name
                        ] = connected_pipeline_input

                        data_flow_graph.add_node(
                            connected_pipeline_input, type=PipelineInputRef.__name__
                        )
                        data_flow_graph_simple.add_node(
                            connected_pipeline_input, type=PipelineInputRef.__name__
                        )
                        if is_constant:
                            constants[
                                pipeline_input_name
                            ] = step.module.get_config_value("constants")[input_name]

                        default_val = step.module.get_config_value("defaults").get(
                            input_name, None
                        )
                        if is_constant and default_val is not None:
                            raise Exception(
                                f"Module config invalid for step '{step.step_id}': both default value and constant provided for input '{input_name}'."
                            )
                        elif default_val is not None:
                            structure_defaults[pipeline_input_name] = default_val

                    step_input_point = StepInputRef(
                        step_id=step.step_id,
                        value_name=input_name,
                        value_schema=schema,
                        connected_pipeline_input=connected_pipeline_input.value_name,
                        connected_outputs=None,
                    )
                    connected_pipeline_input.connected_inputs.append(
                        step_input_point.address
                    )
                    data_flow_graph.add_edge(connected_pipeline_input, step_input_point)
                    data_flow_graph_simple.add_edge(connected_pipeline_input, step)

                data_flow_graph.add_node(step_input_point, type=StepInputRef.__name__)

                steps_details[step.step_id]["inputs"][input_name] = step_input_point

                data_flow_graph.add_edge(step_input_point, step)

            if other_step_dependency:
                for module_id in other_step_dependency:
                    execution_graph.add_edge(module_id, step.step_id)
            else:
                execution_graph.add_edge("__root__", step.step_id)

        # calculate execution order
        path_lengths: Dict[str, int] = {}

        for step in self.steps:

            step_id = step.step_id

            paths = list(nx.all_simple_paths(execution_graph, "__root__", step_id))
            max_steps = max(paths, key=lambda x: len(x))
            path_lengths[step_id] = len(max_steps) - 1

        max_length = max(path_lengths.values())

        for i in range(1, max_length + 1):
            stage: List[str] = [m for m, length in path_lengths.items() if length == i]
            processing_stages.append(stage)
            for _step_id in stage:
                steps_details[_step_id]["processing_stage"] = i
                # steps_details[_step_id]["step"].processing_stage = i

        self._constants = constants
        self._defaults = structure_defaults
        self._steps_details = steps_details
        self._execution_graph = execution_graph
        self._data_flow_graph = data_flow_graph
        self._data_flow_graph_simple = data_flow_graph_simple
        self._processing_stages = processing_stages

        self._get_node_of_type.cache_clear()

        # calculating which steps are always required to execute to compute one of the required pipeline outputs.
        # this is done because in some cases it's possible that some steps can be skipped to execute if they
        # don't have a valid input set, because the inputs downstream they are connecting to are 'non-required'
        # optional_steps = []

        # last_stage = self._processing_stages[-1]
        #
        # step_nodes: List[PipelineStep] = [
        #     node
        #     for node in self._data_flow_graph_simple.nodes
        #     if isinstance(node, PipelineStep)
        # ]
        #
        # all_required_inputs = []
        # for step_id in last_stage:
        #
        #     step = self.get_step(step_id)
        #     step_nodes.remove(step)
        #
        #     for k, s_inp in self.get_step_input_refs(step_id).items():
        #         if not s_inp.value_schema.is_required():
        #             continue
        #         all_required_inputs.append(s_inp)
        #
        # for pipeline_input in self.pipeline_input_refs.values():
        #
        #     for last_step_input in all_required_inputs:
        #         try:
        #             path = nx.shortest_path(
        #                 self._data_flow_graph_simple, pipeline_input, last_step_input
        #             )
        #             for p in path:
        #                 if p in step_nodes:
        #                     step_nodes.remove(p)
        #         except (NetworkXNoPath, NodeNotFound):
        #             pass
        #             # print("NO PATH")
        #             # print(f"{pipeline_input} -> {last_step_input}")
        #
        # for s in step_nodes:
        #     self._steps_details[s.step_id]["required"] = False
        #     # s.required = False
        #
        # for input_name, inp in self.pipeline_input_refs.items():
        #     steps = set()
        #     for ci in inp.connected_inputs:
        #         steps.add(ci.step_id)
        #
        #     optional = True
        #     for step_id in steps:
        #         step = self.get_step(step_id)
        #         if self._steps_details[step_id]["required"]:
        #             optional = False
        #             break
        #     if optional:
        #         inp.value_schema.optional = True

Attributes

constants: Mapping[str, Any] property readonly

data_flow_graph: DiGraph property readonly

data_flow_graph_simple: DiGraph property readonly

defaults: Mapping[str, Any] property readonly

execution_graph: DiGraph property readonly

input_aliases: Dict[str, str] pydantic-field required

The input aliases.

output_aliases: Dict[str, str] pydantic-field required

The output aliases.

pipeline_config: PipelineConfig pydantic-field required

The underlying pipeline config.

pipeline_input_refs: Dict[str, kiara.models.module.pipeline.value_refs.PipelineInputRef] property readonly

pipeline_inputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

pipeline_output_refs: Dict[str, kiara.models.module.pipeline.value_refs.PipelineOutputRef] property readonly

pipeline_outputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

processing_stages: List[List[str]] property readonly

step_ids: Iterable[str] property readonly

steps: List[kiara.models.module.pipeline.PipelineStep] pydantic-field required

The pipeline steps

steps_details: Mapping[str, Any] property readonly

steps_input_refs: Dict[str, kiara.models.module.pipeline.value_refs.StepInputRef] property readonly

steps_output_refs: Dict[str, kiara.models.module.pipeline.value_refs.StepOutputRef] property readonly

Methods

get_processing_stage(self, step_id)

Return the processing stage for the specified step_id.

Returns the stage nr (starting with '1').

Source code in kiara/models/module/pipeline/structure.py

def get_processing_stage(self, step_id: str) -> int:
    """Return the processing stage for the specified step_id.

    Returns the stage nr (starting with '1').
    """

    for index, stage in enumerate(self.processing_stages, start=1):
        if step_id in stage:
            return index

    raise Exception(f"Invalid step id '{step_id}'.")

get_step(self, step_id)

Source code in kiara/models/module/pipeline/structure.py

def get_step(self, step_id: str) -> PipelineStep:

    d = self.steps_details.get(step_id, None)
    if d is None:
        raise Exception(f"No module with id: {step_id}")

    return d["step"]

get_step_details(self, step_id)

Source code in kiara/models/module/pipeline/structure.py

def get_step_details(self, step_id: str) -> Mapping[str, Any]:

    d = self.steps_details.get(step_id, None)
    if d is None:
        raise Exception(f"No module with id: {step_id}")

    return d

get_step_input_refs(self, step_id)

Source code in kiara/models/module/pipeline/structure.py

def get_step_input_refs(self, step_id: str) -> Mapping[str, StepInputRef]:

    d = self.steps_details.get(step_id, None)
    if d is None:
        raise Exception(f"No module with id: {step_id}")

    return d["inputs"]

get_step_output_refs(self, step_id)

Source code in kiara/models/module/pipeline/structure.py

def get_step_output_refs(self, step_id: str) -> Mapping[str, StepOutputRef]:

    d = self.steps_details.get(step_id, None)
    if d is None:
        raise Exception(f"No module with id: {step_id}")

    return d["outputs"]

step_is_required(self, step_id)

Check if the specified step is required, or can be omitted.

Source code in kiara/models/module/pipeline/structure.py

def step_is_required(self, step_id: str) -> bool:
    """Check if the specified step is required, or can be omitted."""

    return self.get_step_details(step_id=step_id)["required"]

validate_pipeline_config(values) classmethod

Source code in kiara/models/module/pipeline/structure.py

@root_validator(pre=True)
def validate_pipeline_config(cls, values):

    pipeline_config = values.get("pipeline_config", None)
    if not pipeline_config:
        raise ValueError("No 'pipeline_config' provided.")

    if len(values) != 1:
        raise ValueError(
            "Only 'pipeline_config' key allowed when creating a pipeline structure object."
        )

    _config: PipelineConfig = pipeline_config
    _steps: List[PipelineStep] = list(_config.steps)

    _input_aliases: Dict[str, str] = dict(_config.input_aliases)
    _output_aliases: Dict[str, str] = dict(_config.output_aliases)

    invalid_input_aliases = [a for a in _input_aliases.values() if "." in a]
    if invalid_input_aliases:
        raise Exception(
            f"Invalid input aliases, aliases can't contain special characters: {', '.join(invalid_input_aliases)}."
        )
    invalid_output_aliases = [a for a in _input_aliases.values() if "." in a]
    if invalid_input_aliases:
        raise Exception(
            f"Invalid input aliases, aliases can't contain special characters: {', '.join(invalid_output_aliases)}."
        )

    valid_input_names = set()
    for step in _steps:
        for input_name in step.module.input_names:
            valid_input_names.add(f"{step.step_id}.{input_name}")
    invalid_input_aliases = [
        a for a in _input_aliases.keys() if a not in valid_input_names
    ]
    if invalid_input_aliases:
        raise Exception(
            f"Invalid input reference(s): {', '.join(invalid_input_aliases)}. Must be one of: {', '.join(valid_input_names)}"
        )
    valid_output_names = set()
    for step in _steps:
        for output_name in step.module.output_names:
            valid_output_names.add(f"{step.step_id}.{output_name}")
    invalid_output_names = [
        a for a in _output_aliases.keys() if a not in valid_output_names
    ]
    if invalid_output_names:
        raise Exception(
            f"Invalid output reference(s): {', '.join(invalid_output_names)}. Must be one of: {', '.join(valid_output_names)}"
        )

    values["steps"] = _steps
    values["input_aliases"] = _input_aliases
    values["output_aliases"] = _output_aliases
    return values

generate_pipeline_endpoint_name(step_id, value_name)

Source code in kiara/models/module/pipeline/structure.py

def generate_pipeline_endpoint_name(step_id: str, value_name: str):

    return f"{step_id}__{value_name}"

value_refs

Classes

PipelineInputRef (ValueRef) pydantic-model

An input to a pipeline.

Source code in kiara/models/module/pipeline/value_refs.py

class PipelineInputRef(ValueRef):
    """An input to a pipeline."""

    connected_inputs: List[StepValueAddress] = Field(
        description="The step inputs that are connected to this pipeline input",
        default_factory=list,
    )
    is_constant: bool = Field(
        "Whether this input is a constant and can't be changed by the user."
    )

    @property
    def alias(self) -> str:
        return generate_step_alias(PIPELINE_PARENT_MARKER, self.value_name)

Attributes

alias: str property readonly

connected_inputs: List[kiara.models.module.pipeline.value_refs.StepValueAddress] pydantic-field

The step inputs that are connected to this pipeline input

is_constant: bool pydantic-field

PipelineOutputRef (ValueRef) pydantic-model

An output to a pipeline.

Source code in kiara/models/module/pipeline/value_refs.py

class PipelineOutputRef(ValueRef):
    """An output to a pipeline."""

    connected_output: StepValueAddress = Field(description="Connected step outputs.")

    @property
    def alias(self) -> str:
        return generate_step_alias(PIPELINE_PARENT_MARKER, self.value_name)

Attributes

alias: str property readonly

connected_output: StepValueAddress pydantic-field required

Connected step outputs.

StepInputRef (ValueRef) pydantic-model

An input to a step.

This object can either have a 'connected_outputs' set, or a 'connected_pipeline_input', not both.

Source code in kiara/models/module/pipeline/value_refs.py

class StepInputRef(ValueRef):
    """An input to a step.

    This object can either have a 'connected_outputs' set, or a 'connected_pipeline_input', not both.
    """

    step_id: str = Field(description="The step id.")
    connected_outputs: Optional[List[StepValueAddress]] = Field(
        default=None,
        description="A potential connected list of one or several module outputs.",
    )
    connected_pipeline_input: Optional[str] = Field(
        default=None, description="A potential pipeline input."
    )
    is_constant: bool = Field(
        "Whether this input is a constant and can't be changed by the user."
    )

    @root_validator(pre=True)
    def ensure_single_connected_item(cls, values):

        if values.get("connected_outputs", None) and values.get(
            "connected_pipeline_input"
        ):
            raise ValueError("Multiple connected items, only one allowed.")

        return values

    @property
    def alias(self) -> str:
        return generate_step_alias(self.step_id, self.value_name)

    @property
    def address(self) -> StepValueAddress:
        return StepValueAddress(step_id=self.step_id, value_name=self.value_name)

    def __str__(self):
        name = camel_case_to_snake_case(self.__class__.__name__[0:-5], repl=" ")
        return f"{name}: {self.step_id}.{self.value_name} ({self.value_schema.type})"

Attributes

address: StepValueAddress property readonly

alias: str property readonly

connected_outputs: List[kiara.models.module.pipeline.value_refs.StepValueAddress] pydantic-field

A potential connected list of one or several module outputs.

connected_pipeline_input: str pydantic-field

A potential pipeline input.

is_constant: bool pydantic-field

step_id: str pydantic-field required

The step id.

ensure_single_connected_item(values) classmethod

Source code in kiara/models/module/pipeline/value_refs.py

@root_validator(pre=True)
def ensure_single_connected_item(cls, values):

    if values.get("connected_outputs", None) and values.get(
        "connected_pipeline_input"
    ):
        raise ValueError("Multiple connected items, only one allowed.")

    return values

StepOutputRef (ValueRef) pydantic-model

An output to a step.

Source code in kiara/models/module/pipeline/value_refs.py

class StepOutputRef(ValueRef):
    """An output to a step."""

    class Config:
        allow_mutation = True

    step_id: str = Field(description="The step id.")
    pipeline_output: Optional[str] = Field(description="The connected pipeline output.")
    connected_inputs: List[StepValueAddress] = Field(
        description="The step inputs that are connected to this step output",
        default_factory=list,
    )

    @property
    def alias(self) -> str:
        return generate_step_alias(self.step_id, self.value_name)

    @property
    def address(self) -> StepValueAddress:
        return StepValueAddress(step_id=self.step_id, value_name=self.value_name)

    def __str__(self):
        name = camel_case_to_snake_case(self.__class__.__name__[0:-5], repl=" ")
        return f"{name}: {self.step_id}.{self.value_name} ({self.value_schema.type})"

Attributes

address: StepValueAddress property readonly

alias: str property readonly

connected_inputs: List[kiara.models.module.pipeline.value_refs.StepValueAddress] pydantic-field

The step inputs that are connected to this step output

pipeline_output: str pydantic-field

The connected pipeline output.

step_id: str pydantic-field required

The step id.

Config

Source code in kiara/models/module/pipeline/value_refs.py

class Config:
    allow_mutation = True

StepValueAddress (BaseModel) pydantic-model

Small model to describe the address of a value of a step, within a Pipeline/PipelineStructure.

Source code in kiara/models/module/pipeline/value_refs.py

class StepValueAddress(BaseModel):
    """Small model to describe the address of a value of a step, within a Pipeline/PipelineStructure."""

    class Config:
        extra = Extra.forbid

    step_id: str = Field(description="The id of a step within a pipeline.")
    value_name: str = Field(
        description="The name of the value (output name or pipeline input name)."
    )
    sub_value: Optional[Dict[str, Any]] = Field(
        default=None,
        description="A reference to a subitem of a value (e.g. column, list item)",
    )

    @property
    def alias(self):
        """An alias string for this address (in the form ``[step_id].[value_name]``)."""
        return generate_step_alias(self.step_id, self.value_name)

    def __eq__(self, other):

        if not isinstance(other, StepValueAddress):
            return False

        return (self.step_id, self.value_name, self.sub_value) == (
            other.step_id,
            other.value_name,
            other.sub_value,
        )

    def __hash__(self):

        return hash((self.step_id, self.value_name, self.sub_value))

    def __repr__(self):

        if self.sub_value:
            sub_value = f" sub_value={self.sub_value}"
        else:
            sub_value = ""
        return f"{self.__class__.__name__}(step_id={self.step_id}, value_name={self.value_name}{sub_value})"

    def __str__(self):
        return self.__repr__()

Attributes

alias property readonly

An alias string for this address (in the form [step_id].[value_name]).

step_id: str pydantic-field required

The id of a step within a pipeline.

sub_value: Dict[str, Any] pydantic-field

A reference to a subitem of a value (e.g. column, list item)

value_name: str pydantic-field required

The name of the value (output name or pipeline input name).

Config

Source code in kiara/models/module/pipeline/value_refs.py

class Config:
    extra = Extra.forbid

ValueRef (BaseModel) pydantic-model

An object that holds information about the location of a value within a pipeline (or other structure).

Basically, a ValueRef helps the containing object where in its structure the value belongs (for example so it can update dependent other values). A ValueRef object (obviously) does not contain the value itself.

There are four different ValueRef type that are relevant for pipelines:

• [kiara.pipeline.values.StepInputRef][]: an input to a step
• [kiara.pipeline.values.StepOutputRef][]: an output of a step
• [kiara.pipeline.values.PipelineInputRef][]: an input to a pipeline
• [kiara.pipeline.values.PipelineOutputRef][]: an output for a pipeline

Several ValueRef objects can target the same value, for example a step output and a connected step input would reference the same Value (in most cases)..

Source code in kiara/models/module/pipeline/value_refs.py

class ValueRef(BaseModel):
    """An object that holds information about the location of a value within a pipeline (or other structure).

    Basically, a `ValueRef` helps the containing object where in its structure the value belongs (for example so
    it can update dependent other values). A `ValueRef` object (obviously) does not contain the value itself.

    There are four different ValueRef type that are relevant for pipelines:

    - [kiara.pipeline.values.StepInputRef][]: an input to a step
    - [kiara.pipeline.values.StepOutputRef][]: an output of a step
    - [kiara.pipeline.values.PipelineInputRef][]: an input to a pipeline
    - [kiara.pipeline.values.PipelineOutputRef][]: an output for a pipeline

    Several `ValueRef` objects can target the same value, for example a step output and a connected step input would
    reference the same `Value` (in most cases)..
    """

    class Config:
        allow_mutation = True
        extra = Extra.forbid

    _id: uuid.UUID = PrivateAttr(default_factory=uuid.uuid4)
    value_name: str
    value_schema: ValueSchema
    # pipeline_id: str

    def __eq__(self, other):

        if not isinstance(other, self.__class__):
            return False

        return self._id == other._id

    def __hash__(self):
        return hash(self._id)

    def __repr__(self):
        step_id = ""
        if hasattr(self, "step_id"):
            step_id = f" step_id='{self.step_id}'"
        return f"{self.__class__.__name__}(value_name='{self.value_name}' {step_id})"

    def __str__(self):
        name = camel_case_to_snake_case(self.__class__.__name__[0:-5], repl=" ")
        return f"{name}: {self.value_name} ({self.value_schema.type})"

value_name: str pydantic-field required

value_schema: ValueSchema pydantic-field required

Config

Source code in kiara/models/module/pipeline/value_refs.py

class Config:
    allow_mutation = True
    extra = Extra.forbid

allow_mutation

extra

generate_step_alias(step_id, value_name)

Source code in kiara/models/module/pipeline/value_refs.py

def generate_step_alias(step_id: str, value_name):
    return f"{step_id}.{value_name}"

python_class

Classes

PythonClass (KiaraModel) pydantic-model

Python class and module information.

Source code in kiara/models/python_class.py

class PythonClass(KiaraModel):
    """Python class and module information."""

    _kiara_model_id = "instance.wrapped_python_class"

    @classmethod
    def from_class(cls, item_cls: Type, attach_context_metadata: bool = False):

        cls_name = item_cls.__name__
        module_name = item_cls.__module__

        if module_name == "builtins":
            full_name = cls_name
        else:
            full_name = f"{item_cls.__module__}.{item_cls.__name__}"

        conf: Dict[str, Any] = {
            "python_class_name": cls_name,
            "python_module_name": module_name,
            "full_name": full_name,
        }

        if attach_context_metadata:
            raise NotImplementedError()
            ctx_md = ContextMetadataModel.from_class(item_cls=item_cls)
            conf["items"] = ctx_md

        result = PythonClass.construct(**conf)
        result._cls_cache = item_cls
        return result

    python_class_name: str = Field(description="The name of the Python class.")
    python_module_name: str = Field(
        description="The name of the Python module this class lives in."
    )
    full_name: str = Field(description="The full class namespace.")

    # context_metadata: Optional[ContextMetadataModel] = Field(
    #     description="Context metadata for the class.", default=None
    # )

    _module_cache: ModuleType = PrivateAttr(default=None)
    _cls_cache: Type = PrivateAttr(default=None)

    def _retrieve_id(self) -> str:
        return self.full_name

    def _retrieve_data_to_hash(self) -> Any:
        return self.full_name

    def get_class(self) -> Type:

        if self._cls_cache is None:
            m = self.get_python_module()
            self._cls_cache = getattr(m, self.python_class_name)
        return self._cls_cache

    def get_python_module(self) -> ModuleType:
        if self._module_cache is None:
            self._module_cache = importlib.import_module(self.python_module_name)
        return self._module_cache

Attributes

full_name: str pydantic-field required

The full class namespace.

python_class_name: str pydantic-field required

The name of the Python class.

python_module_name: str pydantic-field required

The name of the Python module this class lives in.

from_class(item_cls, attach_context_metadata=False) classmethod

Source code in kiara/models/python_class.py

@classmethod
def from_class(cls, item_cls: Type, attach_context_metadata: bool = False):

    cls_name = item_cls.__name__
    module_name = item_cls.__module__

    if module_name == "builtins":
        full_name = cls_name
    else:
        full_name = f"{item_cls.__module__}.{item_cls.__name__}"

    conf: Dict[str, Any] = {
        "python_class_name": cls_name,
        "python_module_name": module_name,
        "full_name": full_name,
    }

    if attach_context_metadata:
        raise NotImplementedError()
        ctx_md = ContextMetadataModel.from_class(item_cls=item_cls)
        conf["items"] = ctx_md

    result = PythonClass.construct(**conf)
    result._cls_cache = item_cls
    return result

get_class(self)

Source code in kiara/models/python_class.py

def get_class(self) -> Type:

    if self._cls_cache is None:
        m = self.get_python_module()
        self._cls_cache = getattr(m, self.python_class_name)
    return self._cls_cache

get_python_module(self)

Source code in kiara/models/python_class.py

def get_python_module(self) -> ModuleType:
    if self._module_cache is None:
        self._module_cache = importlib.import_module(self.python_module_name)
    return self._module_cache

render_value special

Classes

RenderInstruction (KiaraModel) pydantic-model

Source code in kiara/models/render_value/__init__.py

class RenderInstruction(KiaraModel):
    @classmethod
    @abc.abstractmethod
    def retrieve_source_type(cls) -> str:
        pass

    @classmethod
    def retrieve_supported_target_types(cls) -> Iterable[str]:

        result = []
        for attr in dir(cls):
            if len(attr) <= 11 or not attr.startswith("render_as__"):
                continue

            attr = attr[11:]
            target_type = attr[0:]
            result.append(target_type)

        return result

retrieve_source_type() classmethod

Source code in kiara/models/render_value/__init__.py

@classmethod
@abc.abstractmethod
def retrieve_source_type(cls) -> str:
    pass

retrieve_supported_target_types() classmethod

Source code in kiara/models/render_value/__init__.py

@classmethod
def retrieve_supported_target_types(cls) -> Iterable[str]:

    result = []
    for attr in dir(cls):
        if len(attr) <= 11 or not attr.startswith("render_as__"):
            continue

        attr = attr[11:]
        target_type = attr[0:]
        result.append(target_type)

    return result

RenderMetadata (KiaraModel) pydantic-model

Source code in kiara/models/render_value/__init__.py

class RenderMetadata(KiaraModel):

    related_instructions: Dict[str, RenderInstruction] = Field(
        description="Related instructions, to be used by implementing frontends as hints.",
        default_factory=dict,
    )

Attributes

related_instructions: Dict[str, kiara.models.render_value.RenderInstruction] pydantic-field

Related instructions, to be used by implementing frontends as hints.

RenderValueResult (tuple)

RenderValueResult(rendered, metadata)

Source code in kiara/models/render_value/__init__.py

class RenderValueResult(NamedTuple):

    rendered: Any
    metadata: RenderMetadata

metadata: RenderMetadata

rendered: Any

runtime_environment special

logger

RuntimeEnvironment (KiaraModel) pydantic-model

Source code in kiara/models/runtime_environment/__init__.py

class RuntimeEnvironment(KiaraModel):
    class Config:
        underscore_attrs_are_private = False
        allow_mutation = False

    @classmethod
    def get_environment_type_name(cls) -> str:

        env_type = cls.__fields__["environment_type"]
        args = get_args(env_type.type_)
        assert len(args) == 1

        return args[0]

    @classmethod
    def create_environment_model(cls):

        try:
            type_name = cls.get_environment_type_name()
            data = cls.retrieve_environment_data()
            assert (
                "environment_type" not in data.keys()
                or data["environment_keys"] == type_name
            )
            data["environment_type"] = type_name

        except Exception as e:
            raise Exception(f"Can't create environment model for '{cls.__name__}': {e}")
        return cls(**data)

    def get_category_alias(self) -> str:
        return f"{ENVIRONMENT_TYPE_CATEGORY_ID}.{self.environment_type}"  # type: ignore

    @classmethod
    @abstractmethod
    def retrieve_environment_data(cls) -> Dict[str, Any]:
        pass

    def _create_renderable_for_field(
        self, field_name: str, for_summary: bool = False
    ) -> Optional[RenderableType]:

        return extract_renderable(getattr(self, field_name))

    def _retrieve_id(self) -> str:
        return self.__class__.get_environment_type_name()

    def create_renderable(self, **config: Any) -> RenderableType:

        summary = config.get("summary", False)

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("field")
        table.add_column("summary")
        for field_name, field in self.__fields__.items():
            summary_item = self._create_renderable_for_field(
                field_name, for_summary=summary
            )
            if summary_item is not None:
                table.add_row(field_name, summary_item)

        return table

Config

Source code in kiara/models/runtime_environment/__init__.py

class Config:
    underscore_attrs_are_private = False
    allow_mutation = False

allow_mutation

underscore_attrs_are_private

create_environment_model() classmethod

Source code in kiara/models/runtime_environment/__init__.py

@classmethod
def create_environment_model(cls):

    try:
        type_name = cls.get_environment_type_name()
        data = cls.retrieve_environment_data()
        assert (
            "environment_type" not in data.keys()
            or data["environment_keys"] == type_name
        )
        data["environment_type"] = type_name

    except Exception as e:
        raise Exception(f"Can't create environment model for '{cls.__name__}': {e}")
    return cls(**data)

create_renderable(self, **config)

Source code in kiara/models/runtime_environment/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:

    summary = config.get("summary", False)

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("field")
    table.add_column("summary")
    for field_name, field in self.__fields__.items():
        summary_item = self._create_renderable_for_field(
            field_name, for_summary=summary
        )
        if summary_item is not None:
            table.add_row(field_name, summary_item)

    return table

get_category_alias(self)

Source code in kiara/models/runtime_environment/__init__.py

def get_category_alias(self) -> str:
    return f"{ENVIRONMENT_TYPE_CATEGORY_ID}.{self.environment_type}"  # type: ignore

get_environment_type_name() classmethod

Source code in kiara/models/runtime_environment/__init__.py

@classmethod
def get_environment_type_name(cls) -> str:

    env_type = cls.__fields__["environment_type"]
    args = get_args(env_type.type_)
    assert len(args) == 1

    return args[0]

retrieve_environment_data() classmethod

Source code in kiara/models/runtime_environment/__init__.py

@classmethod
@abstractmethod
def retrieve_environment_data(cls) -> Dict[str, Any]:
    pass

Modules

kiara

Classes

ArchiveTypeClassesInfo (TypeInfoModelGroup) pydantic-model

Source code in kiara/models/runtime_environment/kiara.py

class ArchiveTypeClassesInfo(TypeInfoModelGroup):

    _kiara_model_id = "info.archive_types"

    @classmethod
    def base_info_class(cls) -> Type[ArchiveTypeInfo]:
        return ArchiveTypeInfo

    type_name: Literal["archive_type"] = "archive_type"
    type_infos: Mapping[str, ArchiveTypeInfo] = Field(
        description="The archive info instances for each type."
    )

Attributes

type_infos: Mapping[str, kiara.models.runtime_environment.kiara.ArchiveTypeInfo] pydantic-field required

The archive info instances for each type.

type_name: Literal['archive_type'] pydantic-field

base_info_class() classmethod

Source code in kiara/models/runtime_environment/kiara.py

@classmethod
def base_info_class(cls) -> Type[ArchiveTypeInfo]:
    return ArchiveTypeInfo

ArchiveTypeInfo (TypeInfo) pydantic-model

Source code in kiara/models/runtime_environment/kiara.py

class ArchiveTypeInfo(TypeInfo):

    _kiara_model_id = "info.archive_type"

    @classmethod
    def create_from_type_class(self, type_cls: Type[KiaraArchive]) -> "ArchiveTypeInfo":

        authors_md = AuthorsMetadataModel.from_class(type_cls)
        doc = DocumentationMetadataModel.from_class_doc(type_cls)
        python_class = PythonClass.from_class(type_cls)
        properties_md = ContextMetadataModel.from_class(type_cls)
        type_name = type_cls._archive_type_name  # type: ignore

        return ArchiveTypeInfo.construct(
            type_name=type_name,
            documentation=doc,
            authors=authors_md,
            context=properties_md,
            python_class=python_class,
        )

    @classmethod
    def base_class(self) -> Type[KiaraArchive]:
        return KiaraArchive

    @classmethod
    def category_name(cls) -> str:
        return "archive_type"

    is_writable: bool = Field(
        description="Whether this archive is writeable.", default=False
    )
    supported_item_types: List[str] = Field(
        description="The item types this archive suports."
    )

    def create_renderable(self, **config: Any) -> RenderableType:

        include_doc = config.get("include_doc", True)

        table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
        table.add_column("property", style="i")
        table.add_column("value")

        if include_doc:
            table.add_row(
                "Documentation",
                Panel(self.documentation.create_renderable(), box=box.SIMPLE),
            )
        table.add_row("Author(s)", self.authors.create_renderable())
        table.add_row("Context", self.context.create_renderable())

        table.add_row("Python class", self.python_class.create_renderable())

        table.add_row("is_writeable", "yes" if self.is_writable else "no")
        table.add_row(
            "supported_item_types", ", ".join(sorted(self.supported_item_types))
        )

        return table

Attributes

is_writable: bool pydantic-field

Whether this archive is writeable.

supported_item_types: List[str] pydantic-field required

The item types this archive suports.

base_class() classmethod

Source code in kiara/models/runtime_environment/kiara.py

@classmethod
def base_class(self) -> Type[KiaraArchive]:
    return KiaraArchive

category_name() classmethod

Source code in kiara/models/runtime_environment/kiara.py

@classmethod
def category_name(cls) -> str:
    return "archive_type"

create_from_type_class(type_cls) classmethod

Source code in kiara/models/runtime_environment/kiara.py

@classmethod
def create_from_type_class(self, type_cls: Type[KiaraArchive]) -> "ArchiveTypeInfo":

    authors_md = AuthorsMetadataModel.from_class(type_cls)
    doc = DocumentationMetadataModel.from_class_doc(type_cls)
    python_class = PythonClass.from_class(type_cls)
    properties_md = ContextMetadataModel.from_class(type_cls)
    type_name = type_cls._archive_type_name  # type: ignore

    return ArchiveTypeInfo.construct(
        type_name=type_name,
        documentation=doc,
        authors=authors_md,
        context=properties_md,
        python_class=python_class,
    )

create_renderable(self, **config)

Source code in kiara/models/runtime_environment/kiara.py

def create_renderable(self, **config: Any) -> RenderableType:

    include_doc = config.get("include_doc", True)

    table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
    table.add_column("property", style="i")
    table.add_column("value")

    if include_doc:
        table.add_row(
            "Documentation",
            Panel(self.documentation.create_renderable(), box=box.SIMPLE),
        )
    table.add_row("Author(s)", self.authors.create_renderable())
    table.add_row("Context", self.context.create_renderable())

    table.add_row("Python class", self.python_class.create_renderable())

    table.add_row("is_writeable", "yes" if self.is_writable else "no")
    table.add_row(
        "supported_item_types", ", ".join(sorted(self.supported_item_types))
    )

    return table

KiaraTypesRuntimeEnvironment (RuntimeEnvironment) pydantic-model

Source code in kiara/models/runtime_environment/kiara.py

class KiaraTypesRuntimeEnvironment(RuntimeEnvironment):

    _kiara_model_id = "info.runtime.kiara_types"

    environment_type: Literal["kiara_types"]
    archive_types: ArchiveTypeClassesInfo = Field(
        description="The available implemented store types."
    )
    metadata_types: MetadataTypeClassesInfo = Field(
        description="The available metadata types."
    )

    @classmethod
    def retrieve_environment_data(cls) -> Dict[str, Any]:

        result: Dict[str, Any] = {}
        result["metadata_types"] = find_metadata_models()
        result["archive_types"] = find_archive_types()

        return result

Attributes

archive_types: ArchiveTypeClassesInfo pydantic-field required

The available implemented store types.

environment_type: Literal['kiara_types'] pydantic-field required

metadata_types: MetadataTypeClassesInfo pydantic-field required

The available metadata types.

retrieve_environment_data() classmethod

Source code in kiara/models/runtime_environment/kiara.py

@classmethod
def retrieve_environment_data(cls) -> Dict[str, Any]:

    result: Dict[str, Any] = {}
    result["metadata_types"] = find_metadata_models()
    result["archive_types"] = find_archive_types()

    return result

find_archive_types(alias=None, only_for_package=None)

Source code in kiara/models/runtime_environment/kiara.py

def find_archive_types(
    alias: Optional[str] = None, only_for_package: Optional[str] = None
) -> ArchiveTypeClassesInfo:

    archive_types = find_all_archive_types()

    group: ArchiveTypeClassesInfo = ArchiveTypeClassesInfo.create_from_type_items(  # type: ignore
        group_alias=alias, **archive_types
    )

    if only_for_package:
        temp: Dict[str, TypeInfo] = {}
        for key, info in group.items():
            if info.context.labels.get("package") == only_for_package:
                temp[key] = info  # type: ignore

        group = ArchiveTypeClassesInfo.construct(
            group_id=group.group_id, group_alias=group.group_alias, type_infos=temp  # type: ignore
        )

    return group

operating_system

Classes

OSRuntimeEnvironment (RuntimeEnvironment) pydantic-model

Manages information about the OS this kiara instance is running in.

TODO: details for other OS's (mainly BSDs)

Source code in kiara/models/runtime_environment/operating_system.py

class OSRuntimeEnvironment(RuntimeEnvironment):
    """Manages information about the OS this kiara instance is running in.

    # TODO: details for other OS's (mainly BSDs)
    """

    _kiara_model_id = "info.runtime.os"

    environment_type: typing.Literal["operating_system"]
    operation_system: str = Field(description="The operation system name.")
    platform: str = Field(description="The platform name.")
    release: str = Field(description="The platform release name.")
    version: str = Field(description="The platform version name.")
    machine: str = Field(description="The architecture.")
    os_specific: typing.Dict[str, typing.Any] = Field(
        description="OS specific platform metadata.", default_factory=dict
    )

    @classmethod
    def retrieve_environment_data(self) -> typing.Dict[str, typing.Any]:

        os_specific: typing.Dict[str, typing.Any] = {}
        platform_system = platform.system()
        if platform_system == "Linux":
            import distro

            os_specific["distribution"] = {
                "name": distro.name(),
                "version": distro.version(),
                "codename": distro.codename(),
            }
        elif platform_system == "Darwin":
            mac_version = platform.mac_ver()
            os_specific["mac_ver_release"] = mac_version[0]
            os_specific["mac_ver_machine"] = mac_version[2]

        result = {
            "operation_system": os.name,
            "platform": platform_system,
            "release": platform.release(),
            "version": platform.version(),
            "machine": platform.machine(),
            "os_specific": os_specific,
        }

        # if config.include_all_info:
        #     result["uname"] = platform.uname()._asdict()

        return result

Attributes

environment_type: Literal['operating_system'] pydantic-field required

machine: str pydantic-field required

The architecture.

operation_system: str pydantic-field required

The operation system name.

os_specific: Dict[str, Any] pydantic-field

OS specific platform metadata.

platform: str pydantic-field required

The platform name.

release: str pydantic-field required

The platform release name.

version: str pydantic-field required

The platform version name.

retrieve_environment_data() classmethod

Source code in kiara/models/runtime_environment/operating_system.py

@classmethod
def retrieve_environment_data(self) -> typing.Dict[str, typing.Any]:

    os_specific: typing.Dict[str, typing.Any] = {}
    platform_system = platform.system()
    if platform_system == "Linux":
        import distro

        os_specific["distribution"] = {
            "name": distro.name(),
            "version": distro.version(),
            "codename": distro.codename(),
        }
    elif platform_system == "Darwin":
        mac_version = platform.mac_ver()
        os_specific["mac_ver_release"] = mac_version[0]
        os_specific["mac_ver_machine"] = mac_version[2]

    result = {
        "operation_system": os.name,
        "platform": platform_system,
        "release": platform.release(),
        "version": platform.version(),
        "machine": platform.machine(),
        "os_specific": os_specific,
    }

    # if config.include_all_info:
    #     result["uname"] = platform.uname()._asdict()

    return result

python

Classes

PythonPackage (BaseModel) pydantic-model

Source code in kiara/models/runtime_environment/python.py

class PythonPackage(BaseModel):

    name: str = Field(description="The name of the Python package.")
    version: str = Field(description="The version of the package.")

Attributes

name: str pydantic-field required

The name of the Python package.

version: str pydantic-field required

The version of the package.

PythonRuntimeEnvironment (RuntimeEnvironment) pydantic-model

Source code in kiara/models/runtime_environment/python.py

class PythonRuntimeEnvironment(RuntimeEnvironment):

    _kiara_model_id = "info.runtime.python"

    environment_type: Literal["python"]
    python_version: str = Field(description="The version of Python.")
    packages: List[PythonPackage] = Field(
        description="The packages installed in the Python (virtual) environment."
    )
    # python_config: typing.Dict[str, str] = Field(
    #     description="Configuration details about the Python installation."
    # )

    def _create_renderable_for_field(
        self, field_name: str, for_summary: bool = False
    ) -> Optional[RenderableType]:

        if field_name != "packages":
            return extract_renderable(getattr(self, field_name))

        if for_summary:
            return ", ".join(p.name for p in self.packages)

        table = Table(show_header=True, box=box.SIMPLE)
        table.add_column("package name")
        table.add_column("version")

        for package in self.packages:
            table.add_row(package.name, package.version)

        return table

    @classmethod
    def retrieve_environment_data(cls) -> Dict[str, Any]:

        packages = []
        all_packages = packages_distributions()
        for name, pkgs in all_packages.items():
            for pkg in pkgs:
                dist = distribution(pkg)
                packages.append({"name": name, "version": dist.version})

        result: Dict[str, Any] = {
            "python_version": sys.version,
            "packages": sorted(packages, key=lambda x: x["name"]),
        }

        # if config.include_all_info:
        #     import sysconfig
        #     result["python_config"] = sysconfig.get_config_vars()

        return result

Attributes

environment_type: Literal['python'] pydantic-field required

packages: List[kiara.models.runtime_environment.python.PythonPackage] pydantic-field required

The packages installed in the Python (virtual) environment.

python_version: str pydantic-field required

The version of Python.

retrieve_environment_data() classmethod

Source code in kiara/models/runtime_environment/python.py

@classmethod
def retrieve_environment_data(cls) -> Dict[str, Any]:

    packages = []
    all_packages = packages_distributions()
    for name, pkgs in all_packages.items():
        for pkg in pkgs:
            dist = distribution(pkg)
            packages.append({"name": name, "version": dist.version})

    result: Dict[str, Any] = {
        "python_version": sys.version,
        "packages": sorted(packages, key=lambda x: x["name"]),
    }

    # if config.include_all_info:
    #     import sysconfig
    #     result["python_config"] = sysconfig.get_config_vars()

    return result

values special

Classes

ValueStatus (Enum)

An enumeration.

Source code in kiara/models/values/__init__.py

class ValueStatus(Enum):

    UNKNONW = "unknown"
    NOT_SET = "not set"
    NONE = "none"
    DEFAULT = "default"
    SET = "set"

DEFAULT

NONE

NOT_SET

SET

UNKNONW

Modules

data_type

Classes

DataTypeClassInfo (TypeInfo) pydantic-model

Source code in kiara/models/values/data_type.py

class DataTypeClassInfo(TypeInfo[DataType]):

    _kiara_model_id = "info.data_type"

    @classmethod
    def create_from_type_class(
        self, type_cls: Type[DataType], kiara: Optional["Kiara"] = None
    ) -> "DataTypeClassInfo":

        authors = AuthorsMetadataModel.from_class(type_cls)
        doc = DocumentationMetadataModel.from_class_doc(type_cls)
        properties_md = ContextMetadataModel.from_class(type_cls)

        if kiara is not None:
            qual_profiles = kiara.type_registry.get_associated_profiles(type_cls._data_type_name)  # type: ignore
            lineage = kiara.type_registry.get_type_lineage(type_cls._data_type_name)  # type: ignore
        else:
            qual_profiles = None
            lineage = None

        try:
            result = DataTypeClassInfo.construct(
                type_name=type_cls._data_type_name,  # type: ignore
                python_class=PythonClass.from_class(type_cls),
                value_cls=PythonClass.from_class(type_cls.python_class()),
                data_type_config_cls=PythonClass.from_class(
                    type_cls.data_type_config_class()
                ),
                lineage=lineage,  # type: ignore
                qualifier_profiles=qual_profiles,
                documentation=doc,
                authors=authors,
                context=properties_md,
            )
        except Exception as e:
            if isinstance(
                e, TypeError
            ) and "missing 1 required positional argument: 'cls'" in str(e):
                raise Exception(
                    f"Invalid implementation of TypeValue subclass '{type_cls.__name__}': 'python_class' method must be marked as a '@classmethod'. This is a bug."
                )
            raise e

        result._kiara = kiara
        return result

    @classmethod
    def base_class(self) -> Type[DataType]:
        return DataType

    @classmethod
    def category_name(cls) -> str:
        return "data_type"

    value_cls: PythonClass = Field(description="The python class of the value itself.")
    data_type_config_cls: PythonClass = Field(
        description="The python class holding the schema for configuring this type."
    )
    lineage: Optional[List[str]] = Field(description="This types lineage.")
    qualifier_profiles: Optional[Mapping[str, Mapping[str, Any]]] = Field(
        description="A map of qualifier profiles for this data types."
    )
    _kiara: Optional["Kiara"] = PrivateAttr(default=None)

    def _retrieve_id(self) -> str:
        return self.type_name

    def _retrieve_data_to_hash(self) -> Any:
        return self.type_name

    def create_renderable(self, **config: Any) -> RenderableType:

        include_doc = config.get("include_doc", True)

        table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
        table.add_column("property", style="i")
        table.add_column("value")

        if self.lineage:
            table.add_row("lineage", "\n".join(self.lineage[0:]))
        else:
            table.add_row("lineage", "-- n/a --")

        if self.qualifier_profiles:
            qual_table = Table(show_header=False, box=box.SIMPLE)
            qual_table.add_column("name")
            qual_table.add_column("config")
            for name, details in self.qualifier_profiles.items():
                json_details = orjson_dumps(details, option=orjson.OPT_INDENT_2)
                qual_table.add_row(
                    name, Syntax(json_details, "json", background_color="default")
                )
            table.add_row("qualifier profile(s)", qual_table)
        else:
            table.add_row("qualifier profile(s)", "-- n/a --")

        if include_doc:
            table.add_row(
                "Documentation",
                Panel(self.documentation.create_renderable(), box=box.SIMPLE),
            )

        table.add_row("Author(s)", self.authors.create_renderable())
        table.add_row("Context", self.context.create_renderable())

        table.add_row("Python class", self.python_class.create_renderable())
        table.add_row("Config class", self.data_type_config_cls.create_renderable())
        table.add_row("Value class", self.value_cls.create_renderable())

        return table

Attributes

data_type_config_cls: PythonClass pydantic-field required

The python class holding the schema for configuring this type.

lineage: List[str] pydantic-field

This types lineage.

qualifier_profiles: Mapping[str, Mapping[str, Any]] pydantic-field

A map of qualifier profiles for this data types.

value_cls: PythonClass pydantic-field required

The python class of the value itself.

base_class() classmethod

Source code in kiara/models/values/data_type.py

@classmethod
def base_class(self) -> Type[DataType]:
    return DataType

category_name() classmethod

Source code in kiara/models/values/data_type.py

@classmethod
def category_name(cls) -> str:
    return "data_type"

create_from_type_class(type_cls, kiara=None) classmethod

Source code in kiara/models/values/data_type.py

@classmethod
def create_from_type_class(
    self, type_cls: Type[DataType], kiara: Optional["Kiara"] = None
) -> "DataTypeClassInfo":

    authors = AuthorsMetadataModel.from_class(type_cls)
    doc = DocumentationMetadataModel.from_class_doc(type_cls)
    properties_md = ContextMetadataModel.from_class(type_cls)

    if kiara is not None:
        qual_profiles = kiara.type_registry.get_associated_profiles(type_cls._data_type_name)  # type: ignore
        lineage = kiara.type_registry.get_type_lineage(type_cls._data_type_name)  # type: ignore
    else:
        qual_profiles = None
        lineage = None

    try:
        result = DataTypeClassInfo.construct(
            type_name=type_cls._data_type_name,  # type: ignore
            python_class=PythonClass.from_class(type_cls),
            value_cls=PythonClass.from_class(type_cls.python_class()),
            data_type_config_cls=PythonClass.from_class(
                type_cls.data_type_config_class()
            ),
            lineage=lineage,  # type: ignore
            qualifier_profiles=qual_profiles,
            documentation=doc,
            authors=authors,
            context=properties_md,
        )
    except Exception as e:
        if isinstance(
            e, TypeError
        ) and "missing 1 required positional argument: 'cls'" in str(e):
            raise Exception(
                f"Invalid implementation of TypeValue subclass '{type_cls.__name__}': 'python_class' method must be marked as a '@classmethod'. This is a bug."
            )
        raise e

    result._kiara = kiara
    return result

create_renderable(self, **config)

Source code in kiara/models/values/data_type.py

def create_renderable(self, **config: Any) -> RenderableType:

    include_doc = config.get("include_doc", True)

    table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
    table.add_column("property", style="i")
    table.add_column("value")

    if self.lineage:
        table.add_row("lineage", "\n".join(self.lineage[0:]))
    else:
        table.add_row("lineage", "-- n/a --")

    if self.qualifier_profiles:
        qual_table = Table(show_header=False, box=box.SIMPLE)
        qual_table.add_column("name")
        qual_table.add_column("config")
        for name, details in self.qualifier_profiles.items():
            json_details = orjson_dumps(details, option=orjson.OPT_INDENT_2)
            qual_table.add_row(
                name, Syntax(json_details, "json", background_color="default")
            )
        table.add_row("qualifier profile(s)", qual_table)
    else:
        table.add_row("qualifier profile(s)", "-- n/a --")

    if include_doc:
        table.add_row(
            "Documentation",
            Panel(self.documentation.create_renderable(), box=box.SIMPLE),
        )

    table.add_row("Author(s)", self.authors.create_renderable())
    table.add_row("Context", self.context.create_renderable())

    table.add_row("Python class", self.python_class.create_renderable())
    table.add_row("Config class", self.data_type_config_cls.create_renderable())
    table.add_row("Value class", self.value_cls.create_renderable())

    return table

DataTypeClassesInfo (TypeInfoModelGroup) pydantic-model

Source code in kiara/models/values/data_type.py

class DataTypeClassesInfo(TypeInfoModelGroup):

    _kiara_model_id = "info.data_types"

    @classmethod
    def create_from_type_items(
        cls,
        group_alias: Optional[str] = None,
        **items: Type,
    ) -> "TypeInfoModelGroup":

        type_infos = {
            k: cls.base_info_class().create_from_type_class(v) for k, v in items.items()  # type: ignore
        }
        data_types_info = cls.construct(group_alias=group_alias, type_infos=type_infos)  # type: ignore
        return data_types_info

    @classmethod
    def create_augmented_from_type_items(
        cls,
        kiara: Optional["Kiara"] = None,
        group_alias: Optional[str] = None,
        **items: Type,
    ) -> "TypeInfoModelGroup":

        type_infos = {
            k: cls.base_info_class().create_from_type_class(v, kiara=kiara) for k, v in items.items()  # type: ignore
        }
        data_types_info = cls.construct(group_alias=group_alias, type_infos=type_infos)  # type: ignore
        data_types_info._kiara = kiara
        return data_types_info

    @classmethod
    def base_info_class(cls) -> Type[DataTypeClassInfo]:
        return DataTypeClassInfo

    type_name: Literal["data_type"] = "data_type"
    type_infos: Mapping[str, DataTypeClassInfo] = Field(
        description="The data_type info instances for each type."
    )
    _kiara: Optional["Kiara"] = PrivateAttr(default=None)

    def create_renderable(self, **config: Any) -> RenderableType:

        full_doc = config.get("full_doc", False)
        show_subtypes_inline = config.get("show_qualifier_profiles_inline", True)
        show_lineage = config.get("show_type_lineage", True)

        show_lines = full_doc or show_subtypes_inline or show_lineage

        table = Table(show_header=True, box=box.SIMPLE, show_lines=show_lines)
        table.add_column("type name", style="i")

        if show_lineage:
            table.add_column("type lineage")

        if show_subtypes_inline:
            table.add_column("(qualifier) profiles")

        if full_doc:
            table.add_column("documentation")
        else:
            table.add_column("description")

        all_types = self.type_infos.keys()

        for type_name in sorted(all_types):  # type: ignore

            t_md = self.type_infos[type_name]  # type: ignore
            row: List[Any] = [type_name]

            if show_lineage:
                if self._kiara is None:
                    lineage_str = "-- n/a --"
                else:
                    lineage = list(
                        self._kiara.type_registry.get_type_lineage(type_name)
                    )
                    lineage_str = ", ".join(reversed(lineage[1:]))
                row.append(lineage_str)
            if show_subtypes_inline:
                if self._kiara is None:
                    qual_profiles = "-- n/a --"
                else:
                    qual_p = self._kiara.type_registry.get_associated_profiles(
                        data_type_name=type_name
                    ).keys()
                    if qual_p:
                        qual_profiles = "\n".join(qual_p)
                    else:
                        qual_profiles = "-- n/a --"
                row.append(qual_profiles)

            if full_doc:
                md = Markdown(t_md.documentation.full_doc)
            else:
                md = Markdown(t_md.documentation.description)
            row.append(md)
            table.add_row(*row)

        return table

Attributes

type_infos: Mapping[str, kiara.models.values.data_type.DataTypeClassInfo] pydantic-field required

The data_type info instances for each type.

type_name: Literal['data_type'] pydantic-field

base_info_class() classmethod

Source code in kiara/models/values/data_type.py

@classmethod
def base_info_class(cls) -> Type[DataTypeClassInfo]:
    return DataTypeClassInfo

create_augmented_from_type_items(kiara=None, group_alias=None, **items) classmethod

Source code in kiara/models/values/data_type.py

@classmethod
def create_augmented_from_type_items(
    cls,
    kiara: Optional["Kiara"] = None,
    group_alias: Optional[str] = None,
    **items: Type,
) -> "TypeInfoModelGroup":

    type_infos = {
        k: cls.base_info_class().create_from_type_class(v, kiara=kiara) for k, v in items.items()  # type: ignore
    }
    data_types_info = cls.construct(group_alias=group_alias, type_infos=type_infos)  # type: ignore
    data_types_info._kiara = kiara
    return data_types_info

create_from_type_items(group_alias=None, **items) classmethod

Source code in kiara/models/values/data_type.py

@classmethod
def create_from_type_items(
    cls,
    group_alias: Optional[str] = None,
    **items: Type,
) -> "TypeInfoModelGroup":

    type_infos = {
        k: cls.base_info_class().create_from_type_class(v) for k, v in items.items()  # type: ignore
    }
    data_types_info = cls.construct(group_alias=group_alias, type_infos=type_infos)  # type: ignore
    return data_types_info

create_renderable(self, **config)

Source code in kiara/models/values/data_type.py

def create_renderable(self, **config: Any) -> RenderableType:

    full_doc = config.get("full_doc", False)
    show_subtypes_inline = config.get("show_qualifier_profiles_inline", True)
    show_lineage = config.get("show_type_lineage", True)

    show_lines = full_doc or show_subtypes_inline or show_lineage

    table = Table(show_header=True, box=box.SIMPLE, show_lines=show_lines)
    table.add_column("type name", style="i")

    if show_lineage:
        table.add_column("type lineage")

    if show_subtypes_inline:
        table.add_column("(qualifier) profiles")

    if full_doc:
        table.add_column("documentation")
    else:
        table.add_column("description")

    all_types = self.type_infos.keys()

    for type_name in sorted(all_types):  # type: ignore

        t_md = self.type_infos[type_name]  # type: ignore
        row: List[Any] = [type_name]

        if show_lineage:
            if self._kiara is None:
                lineage_str = "-- n/a --"
            else:
                lineage = list(
                    self._kiara.type_registry.get_type_lineage(type_name)
                )
                lineage_str = ", ".join(reversed(lineage[1:]))
            row.append(lineage_str)
        if show_subtypes_inline:
            if self._kiara is None:
                qual_profiles = "-- n/a --"
            else:
                qual_p = self._kiara.type_registry.get_associated_profiles(
                    data_type_name=type_name
                ).keys()
                if qual_p:
                    qual_profiles = "\n".join(qual_p)
                else:
                    qual_profiles = "-- n/a --"
            row.append(qual_profiles)

        if full_doc:
            md = Markdown(t_md.documentation.full_doc)
        else:
            md = Markdown(t_md.documentation.description)
        row.append(md)
        table.add_row(*row)

    return table

info

RENDER_FIELDS: Dict[str, Dict[str, Any]]

Classes

ValueInfo (Value) pydantic-model

Source code in kiara/models/values/info.py

class ValueInfo(Value):

    _kiara_model_id = "info.value"

    @classmethod
    def create_from_value(
        cls,
        kiara: "Kiara",
        value: Value,
        resolve_aliases: bool = True,
        resolve_destinies: bool = True,
    ):

        if resolve_aliases:
            aliases = sorted(
                kiara.alias_registry.find_aliases_for_value_id(value.value_id)
            )
        else:
            aliases = None

        if value.is_stored:
            persisted_details = kiara.data_registry.retrieve_persisted_value_details(
                value_id=value.value_id
            )
        else:
            persisted_details = None

        is_internal = "internal" in kiara.type_registry.get_type_lineage(
            value.data_type_name
        )

        if resolve_destinies:
            destiny_links = kiara.data_registry.find_destinies_for_value(
                value_id=value.value_id
            )
            filtered_destinies = {}
            for alias, value_id in destiny_links.items():
                if (
                    alias in value.property_links.keys()
                    and value_id == value.property_links[alias]
                ):
                    continue
                filtered_destinies[alias] = value_id
        else:
            filtered_destinies = None

        model = ValueInfo.construct(
            value_id=value.value_id,
            kiara_id=value.kiara_id,
            value_schema=value.value_schema,
            value_status=value.value_status,
            value_size=value.value_size,
            value_hash=value.value_hash,
            pedigree=value.pedigree,
            pedigree_output_name=value.pedigree_output_name,
            data_type_class=value.data_type_class,
            property_links=value.property_links,
            destiny_links=filtered_destinies,
            destiny_backlinks=value.destiny_backlinks,
            aliases=aliases,
            serialized=persisted_details,
        )
        model._set_registry(value._data_registry)
        model._alias_registry = kiara.alias_registry  # type: ignore
        model._is_stored = value._is_stored
        model._data_type = value._data_type
        model._value_data = value._value_data
        model._data_retrieved = value._data_retrieved
        model._is_internal = is_internal
        return model

    value_id: uuid.UUID = Field(description="The value id.")
    value_schema: ValueSchema = Field(description="The data schema of this value.")
    aliases: Optional[List[str]] = Field(
        description="The aliases that are registered for this value."
    )
    serialized: Optional[PersistedData] = Field(
        description="Details for the serialization process that was used for this value."
    )
    destiny_links: Optional[Mapping[str, uuid.UUID]] = Field(
        description="References to all the values that act as destiny for this value in this context."
    )

    _is_internal: bool = PrivateAttr(default=False)
    _alias_registry: AliasRegistry = PrivateAttr(default=None)

    def _retrieve_id(self) -> str:
        return str(self.value_id)

    def _retrieve_data_to_hash(self) -> Any:
        return self.value_id.bytes

    def resolve_aliases(self):
        aliases = self._alias_registry.find_aliases_for_value_id(self.value_id)
        if aliases:
            aliases = sorted(aliases)
        self.aliases = aliases

    def resolve_destinies(self):
        destiny_links = self._data_registry.find_destinies_for_value(
            value_id=self.value_id
        )
        filtered_destinies = {}
        for alias, value_id in destiny_links.items():
            if (
                alias in self.property_links.keys()
                and value_id == self.property_links[alias]
            ):
                continue
            filtered_destinies[alias] = value_id
        self.destiny_links = filtered_destinies

Attributes

aliases: List[str] pydantic-field

The aliases that are registered for this value.

destiny_links: Mapping[str, uuid.UUID] pydantic-field

References to all the values that act as destiny for this value in this context.

serialized: PersistedData pydantic-field

Details for the serialization process that was used for this value.

create_from_value(kiara, value, resolve_aliases=True, resolve_destinies=True) classmethod

Source code in kiara/models/values/info.py

@classmethod
def create_from_value(
    cls,
    kiara: "Kiara",
    value: Value,
    resolve_aliases: bool = True,
    resolve_destinies: bool = True,
):

    if resolve_aliases:
        aliases = sorted(
            kiara.alias_registry.find_aliases_for_value_id(value.value_id)
        )
    else:
        aliases = None

    if value.is_stored:
        persisted_details = kiara.data_registry.retrieve_persisted_value_details(
            value_id=value.value_id
        )
    else:
        persisted_details = None

    is_internal = "internal" in kiara.type_registry.get_type_lineage(
        value.data_type_name
    )

    if resolve_destinies:
        destiny_links = kiara.data_registry.find_destinies_for_value(
            value_id=value.value_id
        )
        filtered_destinies = {}
        for alias, value_id in destiny_links.items():
            if (
                alias in value.property_links.keys()
                and value_id == value.property_links[alias]
            ):
                continue
            filtered_destinies[alias] = value_id
    else:
        filtered_destinies = None

    model = ValueInfo.construct(
        value_id=value.value_id,
        kiara_id=value.kiara_id,
        value_schema=value.value_schema,
        value_status=value.value_status,
        value_size=value.value_size,
        value_hash=value.value_hash,
        pedigree=value.pedigree,
        pedigree_output_name=value.pedigree_output_name,
        data_type_class=value.data_type_class,
        property_links=value.property_links,
        destiny_links=filtered_destinies,
        destiny_backlinks=value.destiny_backlinks,
        aliases=aliases,
        serialized=persisted_details,
    )
    model._set_registry(value._data_registry)
    model._alias_registry = kiara.alias_registry  # type: ignore
    model._is_stored = value._is_stored
    model._data_type = value._data_type
    model._value_data = value._value_data
    model._data_retrieved = value._data_retrieved
    model._is_internal = is_internal
    return model

resolve_aliases(self)

Source code in kiara/models/values/info.py

def resolve_aliases(self):
    aliases = self._alias_registry.find_aliases_for_value_id(self.value_id)
    if aliases:
        aliases = sorted(aliases)
    self.aliases = aliases

resolve_destinies(self)

Source code in kiara/models/values/info.py

def resolve_destinies(self):
    destiny_links = self._data_registry.find_destinies_for_value(
        value_id=self.value_id
    )
    filtered_destinies = {}
    for alias, value_id in destiny_links.items():
        if (
            alias in self.property_links.keys()
            and value_id == self.property_links[alias]
        ):
            continue
        filtered_destinies[alias] = value_id
    self.destiny_links = filtered_destinies

ValuesInfo (BaseModel) pydantic-model

Source code in kiara/models/values/info.py

class ValuesInfo(BaseModel):
    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps

    @classmethod
    def create_from_values(cls, kiara: "Kiara", *values: Union[Value, uuid.UUID]):

        v = [
            ValueInfo.create_from_value(
                kiara=kiara,
                value=v if isinstance(v, Value) else kiara.data_registry.get_value(v),
            )
            for v in values
        ]
        return ValuesInfo(__root__=v)

    __root__: List[ValueInfo]

    def create_render_map(self, render_type: str = "terminal", **render_config):

        list_by_alias = render_config.get("list_by_alias", True)
        show_internal = render_config.get("show_internal", False)

        render_fields = render_config.get("render_fields", None)
        if not render_fields:
            render_fields = [k for k, v in RENDER_FIELDS.items() if v["show_default"]]
            if list_by_alias:
                render_fields[0] = "aliases"
                render_fields[1] = "value_id"

        render_map: Dict[uuid.UUID, Dict[str, Any]] = {}

        lookup = {}
        for value in self.__root__:
            if not show_internal and value._is_internal:
                continue
            lookup[value.value_id] = value
            details = {}
            for property in render_fields:

                if hasattr(value, property) and property != "data":
                    attr = getattr(value, property)
                else:
                    attr = value
                render_func = (
                    RENDER_FIELDS.get(property, {})
                    .get("render", {})
                    .get(render_type, None)
                )
                if render_func is None:
                    rendered = attr
                else:
                    rendered = render_func(attr)
                details[property] = rendered
            render_map[value.value_id] = details

        if not list_by_alias:
            return {str(k): v for k, v in render_map.items()}
        else:
            result: Dict[str, Dict[str, Any]] = {}
            for value_id, render_details in render_map.items():
                value_aliases = lookup[value_id].aliases
                if value_aliases:
                    for alias in value_aliases:
                        assert alias not in result.keys()
                        render_details = dict(render_details)
                        render_details["alias"] = alias
                        result[alias] = render_details
                else:
                    render_details["alias"] = ""
                    result[f"no_aliases_{value_id}"] = render_details
            return result

    def create_renderable(self, render_type: str = "terminal", **render_config: Any):

        render_map = self.create_render_map(render_type=render_type, **render_config)

        list_by_alias = render_config.get("list_by_alias", True)
        render_fields = render_config.get("render_fields", None)
        if not render_fields:
            render_fields = [k for k, v in RENDER_FIELDS.items() if v["show_default"]]
        if list_by_alias:
            render_fields.insert(0, "alias")
            render_fields.remove("aliases")

        table = Table(box=box.SIMPLE)
        for property in render_fields:
            if property == "aliases" and list_by_alias:
                table.add_column("alias")
            else:
                table.add_column(property)

        for item_id, details in render_map.items():
            row = []
            for field in render_fields:
                value = details[field]
                row.append(value)
            table.add_row(*row)

        return table

Config

Source code in kiara/models/values/info.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/models/values/info.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

create_from_values(kiara, *values) classmethod

Source code in kiara/models/values/info.py

@classmethod
def create_from_values(cls, kiara: "Kiara", *values: Union[Value, uuid.UUID]):

    v = [
        ValueInfo.create_from_value(
            kiara=kiara,
            value=v if isinstance(v, Value) else kiara.data_registry.get_value(v),
        )
        for v in values
    ]
    return ValuesInfo(__root__=v)

create_render_map(self, render_type='terminal', **render_config)

Source code in kiara/models/values/info.py

def create_render_map(self, render_type: str = "terminal", **render_config):

    list_by_alias = render_config.get("list_by_alias", True)
    show_internal = render_config.get("show_internal", False)

    render_fields = render_config.get("render_fields", None)
    if not render_fields:
        render_fields = [k for k, v in RENDER_FIELDS.items() if v["show_default"]]
        if list_by_alias:
            render_fields[0] = "aliases"
            render_fields[1] = "value_id"

    render_map: Dict[uuid.UUID, Dict[str, Any]] = {}

    lookup = {}
    for value in self.__root__:
        if not show_internal and value._is_internal:
            continue
        lookup[value.value_id] = value
        details = {}
        for property in render_fields:

            if hasattr(value, property) and property != "data":
                attr = getattr(value, property)
            else:
                attr = value
            render_func = (
                RENDER_FIELDS.get(property, {})
                .get("render", {})
                .get(render_type, None)
            )
            if render_func is None:
                rendered = attr
            else:
                rendered = render_func(attr)
            details[property] = rendered
        render_map[value.value_id] = details

    if not list_by_alias:
        return {str(k): v for k, v in render_map.items()}
    else:
        result: Dict[str, Dict[str, Any]] = {}
        for value_id, render_details in render_map.items():
            value_aliases = lookup[value_id].aliases
            if value_aliases:
                for alias in value_aliases:
                    assert alias not in result.keys()
                    render_details = dict(render_details)
                    render_details["alias"] = alias
                    result[alias] = render_details
            else:
                render_details["alias"] = ""
                result[f"no_aliases_{value_id}"] = render_details
        return result

create_renderable(self, render_type='terminal', **render_config)

Source code in kiara/models/values/info.py

def create_renderable(self, render_type: str = "terminal", **render_config: Any):

    render_map = self.create_render_map(render_type=render_type, **render_config)

    list_by_alias = render_config.get("list_by_alias", True)
    render_fields = render_config.get("render_fields", None)
    if not render_fields:
        render_fields = [k for k, v in RENDER_FIELDS.items() if v["show_default"]]
    if list_by_alias:
        render_fields.insert(0, "alias")
        render_fields.remove("aliases")

    table = Table(box=box.SIMPLE)
    for property in render_fields:
        if property == "aliases" and list_by_alias:
            table.add_column("alias")
        else:
            table.add_column(property)

    for item_id, details in render_map.items():
        row = []
        for field in render_fields:
            value = details[field]
            row.append(value)
        table.add_row(*row)

    return table

render_value_data(value)

Source code in kiara/models/values/info.py

def render_value_data(value: Value):

    try:
        renderable = value._data_registry.pretty_print_data(
            value.value_id, target_type="terminal_renderable"
        )
    except Exception as e:

        if is_debug():
            import traceback

            traceback.print_exc()
        log_message("error.pretty_print", value=value.value_id, error=e)
        renderable = [str(value.data)]

    return renderable

lineage

COLOR_LIST

ValueLineage

Source code in kiara/models/values/lineage.py

class ValueLineage(object):
    @classmethod
    def from_value(cls, value: Value) -> "ValueLineage":

        pass

    def __init__(self, kiara: Kiara, value: Value):

        self._value: Value = value
        self._kiara: Kiara = kiara

    def create_renderable(self, **config: Any) -> RenderableType:

        include_ids: bool = config.get("include_ids", False)
        tree = fill_lineage_tree(
            kiara=self._kiara, pedigree=self._value.pedigree, include_ids=include_ids
        )
        return tree

create_renderable(self, **config)

Source code in kiara/models/values/lineage.py

def create_renderable(self, **config: Any) -> RenderableType:

    include_ids: bool = config.get("include_ids", False)
    tree = fill_lineage_tree(
        kiara=self._kiara, pedigree=self._value.pedigree, include_ids=include_ids
    )
    return tree

from_value(value) classmethod

Source code in kiara/models/values/lineage.py

@classmethod
def from_value(cls, value: Value) -> "ValueLineage":

    pass

fill_lineage_tree(kiara, pedigree, node=None, include_ids=False, level=0)

Source code in kiara/models/values/lineage.py

def fill_lineage_tree(
    kiara: Kiara,
    pedigree: ValuePedigree,
    node: Optional[Tree] = None,
    include_ids: bool = False,
    level: int = 0,
):

    color = COLOR_LIST[level % len(COLOR_LIST)]
    title = f"[b {color}]{pedigree.module_type}[/b {color}]"
    if node is None:
        main = Tree(title)
    else:
        main = node.add(title)

    for input_name in sorted(pedigree.inputs.keys()):

        child_value_id = pedigree.inputs[input_name]

        child_value = kiara.data_registry.get_value(child_value_id)

        value_type = child_value.data_type_name
        if include_ids:
            v_id_str = f" = {child_value.value_id}"
        else:
            v_id_str = ""
        input_node = main.add(
            f"input: [i {color}]{input_name} ({value_type})[/i {color}]{v_id_str}"
        )
        if child_value.pedigree != ORPHAN:
            fill_lineage_tree(
                kiara=kiara,
                pedigree=child_value.pedigree,
                node=input_node,
                level=level + 1,
                include_ids=include_ids,
            )

    return main

value

ORPHAN

SERIALIZE_TYPES

log

yaml

Classes

PersistedData (SerializedData) pydantic-model

Source code in kiara/models/values/value.py

class PersistedData(SerializedData):

    _kiara_model_id = "instance.persisted_data"

    archive_id: uuid.UUID = Field(
        description="The id of the store that persisted the data."
    )
    chunk_id_map: Mapping[str, SerializedChunkIDs] = Field(
        description="Reference-ids that resolve to the values' serialized chunks."
    )

    def get_keys(self) -> Iterable[str]:
        return self.chunk_id_map.keys()

    def get_serialized_data(self, key: str) -> SerializedChunks:
        return self.chunk_id_map[key]

Attributes

archive_id: UUID pydantic-field required

The id of the store that persisted the data.

chunk_id_map: Mapping[str, kiara.models.values.value.SerializedChunkIDs] pydantic-field required

Reference-ids that resolve to the values' serialized chunks.

get_keys(self)

Source code in kiara/models/values/value.py

def get_keys(self) -> Iterable[str]:
    return self.chunk_id_map.keys()

get_serialized_data(self, key)

Source code in kiara/models/values/value.py

def get_serialized_data(self, key: str) -> SerializedChunks:
    return self.chunk_id_map[key]

SerializationMetadata (KiaraModel) pydantic-model

Source code in kiara/models/values/value.py

class SerializationMetadata(KiaraModel):

    _kiara_model_id = "metadata.serialized_data"

    environment: Mapping[str, int] = Field(
        description="Hash(es) for the environments the value was created/serialized.",
        default_factory=dict,
    )
    deserialize: Mapping[str, Manifest] = Field(
        description="Suggested manifest configs to use to de-serialize the data.",
        default_factory=dict,
    )

Attributes

deserialize: Mapping[str, kiara.models.module.manifest.Manifest] pydantic-field

Suggested manifest configs to use to de-serialize the data.

environment: Mapping[str, int] pydantic-field

Hash(es) for the environments the value was created/serialized.

SerializationResult (SerializedData) pydantic-model

Source code in kiara/models/values/value.py

class SerializationResult(SerializedData):

    _kiara_model_id = "instance.serialization_result"

    data: Dict[
        str,
        Union[
            SerializedBytes,
            SerializedListOfBytes,
            SerializedFile,
            SerializedFiles,
            SerializedInlineJson,
        ],
    ] = Field(
        description="One or several byte arrays representing the serialized state of the value."
    )

    def get_keys(self) -> Iterable[str]:
        return self.data.keys()

    def get_serialized_data(self, key: str) -> SerializedChunks:
        return self.data[key]

    @root_validator(pre=True)
    def validate_data(cls, values):

        codec = values.get("codec", None)
        if codec is None:
            codec = "sha2-256"
            values["hash_codec"] = codec

        v = values.get("data")
        assert isinstance(v, Mapping)

        result = {}
        for field_name, data in v.items():
            if isinstance(data, SerializedChunks):
                result[field_name] = data
            elif isinstance(data, Mapping):
                s_type = data.get("type", None)
                if not s_type:
                    raise ValueError(
                        f"Invalid serialized data config, missing 'type' key: {data}"
                    )

                if s_type not in SERIALIZE_TYPES.keys():
                    raise ValueError(
                        f"Invalid serialized data type '{s_type}'. Allowed types: {', '.join(SERIALIZE_TYPES.keys())}"
                    )

                assert s_type != "chunk-ids"
                cls = SERIALIZE_TYPES[s_type]
                result[field_name] = cls(**data)

        values["data"] = result
        return values

    def create_renderable(self, **config: Any) -> RenderableType:

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("key")
        table.add_column("value")
        table.add_row("data_type", self.data_type)
        _config = Syntax(
            orjson_dumps(self.data_type_config), "json", background_color="default"
        )
        table.add_row("data_type_config", _config)

        data_fields = {}
        for field, model in self.data.items():
            data_fields[field] = {"type": model.type}
        data_json = Syntax(
            orjson_dumps(data_fields), "json", background_color="default"
        )
        table.add_row("data", data_json)
        table.add_row("size", str(self.data_size))
        table.add_row("hash", self.instance_id)

        return table

    def __repr__(self):

        return f"{self.__class__.__name__}(type={self.data_type} size={self.data_size})"

    def __str__(self):
        return self.__repr__()

Attributes

data: Dict[str, Union[kiara.models.values.value.SerializedBytes, kiara.models.values.value.SerializedListOfBytes, kiara.models.values.value.SerializedFile, kiara.models.values.value.SerializedFiles, kiara.models.values.value.SerializedInlineJson]] pydantic-field required

One or several byte arrays representing the serialized state of the value.

create_renderable(self, **config)

Source code in kiara/models/values/value.py

def create_renderable(self, **config: Any) -> RenderableType:

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("key")
    table.add_column("value")
    table.add_row("data_type", self.data_type)
    _config = Syntax(
        orjson_dumps(self.data_type_config), "json", background_color="default"
    )
    table.add_row("data_type_config", _config)

    data_fields = {}
    for field, model in self.data.items():
        data_fields[field] = {"type": model.type}
    data_json = Syntax(
        orjson_dumps(data_fields), "json", background_color="default"
    )
    table.add_row("data", data_json)
    table.add_row("size", str(self.data_size))
    table.add_row("hash", self.instance_id)

    return table

get_keys(self)

Source code in kiara/models/values/value.py

def get_keys(self) -> Iterable[str]:
    return self.data.keys()

get_serialized_data(self, key)

Source code in kiara/models/values/value.py

def get_serialized_data(self, key: str) -> SerializedChunks:
    return self.data[key]

validate_data(values) classmethod

Source code in kiara/models/values/value.py

@root_validator(pre=True)
def validate_data(cls, values):

    codec = values.get("codec", None)
    if codec is None:
        codec = "sha2-256"
        values["hash_codec"] = codec

    v = values.get("data")
    assert isinstance(v, Mapping)

    result = {}
    for field_name, data in v.items():
        if isinstance(data, SerializedChunks):
            result[field_name] = data
        elif isinstance(data, Mapping):
            s_type = data.get("type", None)
            if not s_type:
                raise ValueError(
                    f"Invalid serialized data config, missing 'type' key: {data}"
                )

            if s_type not in SERIALIZE_TYPES.keys():
                raise ValueError(
                    f"Invalid serialized data type '{s_type}'. Allowed types: {', '.join(SERIALIZE_TYPES.keys())}"
                )

            assert s_type != "chunk-ids"
            cls = SERIALIZE_TYPES[s_type]
            result[field_name] = cls(**data)

    values["data"] = result
    return values

SerializedBytes (SerializedPreStoreChunks) pydantic-model

Source code in kiara/models/values/value.py

class SerializedBytes(SerializedPreStoreChunks):

    type: Literal["chunk"] = "chunk"
    chunk: bytes = Field(description="A byte-array")

    def get_chunks(
        self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
    ) -> Iterable[Union[str, BytesLike]]:

        if as_files is False:
            return [self.chunk]
        else:
            if as_files is True:
                file = None
            elif isinstance(as_files, str):
                file = as_files
            else:
                assert len(as_files) == 1
                file = as_files[0]
            path = self._store_bytes_to_file([self.chunk], file=file)
            return path

    def get_number_of_chunks(self) -> int:
        return 1

    def _get_size(self) -> int:
        return len(self.chunk)

    def _create_cids(self, hash_codec: str) -> Sequence[CID]:
        return [self._create_cid_from_chunk(self.chunk, hash_codec=hash_codec)]

Attributes

chunk: bytes pydantic-field required

A byte-array

type: Literal['chunk'] pydantic-field

Methods

get_chunks(self, as_files=True, symlink_ok=True)

Retrieve the chunks belonging to this data instance.

If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of 'as_file' is also ok, otherwise copy the content.

Source code in kiara/models/values/value.py

def get_chunks(
    self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
) -> Iterable[Union[str, BytesLike]]:

    if as_files is False:
        return [self.chunk]
    else:
        if as_files is True:
            file = None
        elif isinstance(as_files, str):
            file = as_files
        else:
            assert len(as_files) == 1
            file = as_files[0]
        path = self._store_bytes_to_file([self.chunk], file=file)
        return path

get_number_of_chunks(self)

Source code in kiara/models/values/value.py

def get_number_of_chunks(self) -> int:
    return 1

SerializedChunkIDs (SerializedChunks) pydantic-model

Source code in kiara/models/values/value.py

class SerializedChunkIDs(SerializedChunks):

    type: Literal["chunk-ids"] = "chunk-ids"
    chunk_id_list: List[str] = Field(
        description="A list of chunk ids, which will be resolved via the attached data registry."
    )
    archive_id: Optional[uuid.UUID] = Field(
        description="The preferred data archive to get the chunks from."
    )
    size: int = Field(description="The size of all chunks combined.")
    _data_registry: "DataRegistry" = PrivateAttr(default=None)

    def get_chunks(
        self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
    ) -> Iterable[Union[str, BytesLike]]:

        if isinstance(as_files, (bool, str)):
            return (
                self._data_registry.retrieve_chunk(
                    chunk_id=chunk,
                    archive_id=self.archive_id,
                    as_file=as_files,
                    symlink_ok=symlink_ok,
                )
                for chunk in self.chunk_id_list
            )
        else:
            result = []
            for idx, chunk_id in enumerate(self.chunk_id_list):
                file = as_files[idx]
                self._data_registry.retrieve_chunk(
                    chunk_id=chunk_id,
                    archive_id=self.archive_id,
                    as_file=file,
                    symlink_ok=symlink_ok,
                )
                result.append(file)
            return result

    def get_number_of_chunks(self) -> int:
        return len(self.chunk_id_list)

    def _get_size(self) -> int:
        return self.size

    def _create_cids(self, hash_codec: str) -> Sequence[CID]:

        result = []
        for chunk_id in self.chunk_id_list:
            cid = CID.decode(chunk_id)
            result.append(cid)

        return result

Attributes

archive_id: UUID pydantic-field

The preferred data archive to get the chunks from.

chunk_id_list: List[str] pydantic-field required

A list of chunk ids, which will be resolved via the attached data registry.

size: int pydantic-field required

The size of all chunks combined.

type: Literal['chunk-ids'] pydantic-field

Methods

get_chunks(self, as_files=True, symlink_ok=True)

Retrieve the chunks belonging to this data instance.

If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of 'as_file' is also ok, otherwise copy the content.

Source code in kiara/models/values/value.py

def get_chunks(
    self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
) -> Iterable[Union[str, BytesLike]]:

    if isinstance(as_files, (bool, str)):
        return (
            self._data_registry.retrieve_chunk(
                chunk_id=chunk,
                archive_id=self.archive_id,
                as_file=as_files,
                symlink_ok=symlink_ok,
            )
            for chunk in self.chunk_id_list
        )
    else:
        result = []
        for idx, chunk_id in enumerate(self.chunk_id_list):
            file = as_files[idx]
            self._data_registry.retrieve_chunk(
                chunk_id=chunk_id,
                archive_id=self.archive_id,
                as_file=file,
                symlink_ok=symlink_ok,
            )
            result.append(file)
        return result

get_number_of_chunks(self)

Source code in kiara/models/values/value.py

def get_number_of_chunks(self) -> int:
    return len(self.chunk_id_list)

SerializedChunks (BaseModel, ABC) pydantic-model

Source code in kiara/models/values/value.py

class SerializedChunks(BaseModel, abc.ABC):
    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps
        extra = Extra.forbid

    _size_cache: Optional[int] = PrivateAttr(default=None)
    _hashes_cache: Dict[str, Sequence[CID]] = PrivateAttr(default_factory=dict)

    @abc.abstractmethod
    def get_chunks(
        self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
    ) -> Iterable[Union[str, BytesLike]]:
        """Retrieve the chunks belonging to this data instance.

        If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use
        an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into
        a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of
        'as_file' is also ok, otherwise copy the content.
        """

    @abc.abstractmethod
    def get_number_of_chunks(self) -> int:
        pass

    @abc.abstractmethod
    def _get_size(self) -> int:
        pass

    @abc.abstractmethod
    def _create_cids(self, hash_codec: str) -> Sequence[CID]:
        pass

    def get_size(self) -> int:

        if self._size_cache is None:
            self._size_cache = self._get_size()
        return self._size_cache

    def get_cids(self, hash_codec: str) -> Sequence[CID]:

        if self._hashes_cache.get(hash_codec, None) is None:
            self._hashes_cache[hash_codec] = self._create_cids(hash_codec=hash_codec)
        return self._hashes_cache[hash_codec]

    def _store_bytes_to_file(
        self, chunks: Iterable[bytes], file: Optional[str] = None
    ) -> str:
        "Utility method to store bytes to a file."

        if file is None:
            file_desc, file = tempfile.mkstemp()

            def del_temp_file():
                os.remove(file)

            atexit.register(del_temp_file)

        else:
            if os.path.exists(file):
                raise Exception(f"Can't write to file, file exists: {file}")
            file_desc = os.open(file, 0o600)

        with os.fdopen(file_desc, "wb") as tmp:
            for chunk in chunks:
                tmp.write(chunk)

        return file

    def _read_bytes_from_file(self, file: str) -> bytes:

        with open(file, "rb") as f:
            content = f.read()

        return content

    # @property
    # def data_hashes(self) -> Iterable[bytes]:
    #
    #     if self._hash_cache is not None:
    #         return self._hash_cache
    #
    #     result = []
    #     size = 0
    #     for chunk in self.get_chunks():
    #         _hash = multihash.digest(chunk, self.codec)
    #         size = size + len(chunk)
    #         result.append(_hash)
    #
    #     if self._size_cache is None:
    #         self._size_cache = size
    #     else:
    #         assert self._size_cache == size
    #
    #     self._hash_cache = result
    #     return self._hash_cache

    # @property
    # def data_size(self) -> int:
    #
    #     if self._size_cache is not None:
    #         return self._size_cache
    #
    #     size = 0
    #     for chunk in self.get_chunks():
    #         size = size + len(chunk)
    #
    #     self._size_cache = size
    #     return self._size_cache

Config

Source code in kiara/models/values/value.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps
    extra = Extra.forbid

extra

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/models/values/value.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

Methods

get_chunks(self, as_files=True, symlink_ok=True)

Retrieve the chunks belonging to this data instance.

If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of 'as_file' is also ok, otherwise copy the content.

Source code in kiara/models/values/value.py

@abc.abstractmethod
def get_chunks(
    self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
) -> Iterable[Union[str, BytesLike]]:
    """Retrieve the chunks belonging to this data instance.

    If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use
    an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into
    a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of
    'as_file' is also ok, otherwise copy the content.
    """

get_cids(self, hash_codec)

Source code in kiara/models/values/value.py

def get_cids(self, hash_codec: str) -> Sequence[CID]:

    if self._hashes_cache.get(hash_codec, None) is None:
        self._hashes_cache[hash_codec] = self._create_cids(hash_codec=hash_codec)
    return self._hashes_cache[hash_codec]

get_number_of_chunks(self)

Source code in kiara/models/values/value.py

@abc.abstractmethod
def get_number_of_chunks(self) -> int:
    pass

get_size(self)

Source code in kiara/models/values/value.py

def get_size(self) -> int:

    if self._size_cache is None:
        self._size_cache = self._get_size()
    return self._size_cache

SerializedData (KiaraModel) pydantic-model

Source code in kiara/models/values/value.py

class SerializedData(KiaraModel):

    data_type: str = Field(
        description="The name of the data type for this serialized value."
    )
    data_type_config: Mapping[str, Any] = Field(
        description="The (optional) config for the data type for this serialized value.",
        default_factory=dict,
    )
    serialization_profile: str = Field(
        description="An identifying name for the serialization method used."
    )
    metadata: SerializationMetadata = Field(
        description="Optional metadata describing aspects of the serialization used.",
        default_factory=dict,
    )

    hash_codec: str = Field(
        description="The codec used to hash the value.", default="sha2-256"
    )
    _cids_cache: Dict[str, Sequence[CID]] = PrivateAttr(default_factory=dict)

    _cached_data_size: Optional[int] = PrivateAttr(default=None)
    _cached_dag: Optional[Dict[str, Sequence[CID]]] = PrivateAttr(default=None)
    # _cached_cid: Optional[CID] = PrivateAttr(default=None)

    def _retrieve_data_to_hash(self) -> Any:

        return self.dag

    @property
    def data_size(self) -> int:
        if self._cached_data_size is not None:
            return self._cached_data_size

        size = 0
        for k in self.get_keys():
            model = self.get_serialized_data(k)
            size = size + model.get_size()
        self._cached_data_size = size
        return self._cached_data_size

    @abc.abstractmethod
    def get_keys(self) -> Iterable[str]:
        pass

    @abc.abstractmethod
    def get_serialized_data(self, key: str) -> SerializedChunks:
        pass

    # @property
    # def cid(self) -> CID:
    #
    #     if self._cached_cid is not None:
    #         return self._cached_cid
    #
    #     # TODO: check whether that is correect, or whether it needs another wrapping in an 'identity' type
    #     codec = multicodec.get("dag-cbor")
    #
    #     hash_func = Multihash(codec=self.hash_codec).digest
    #     hash = hash_func(self.dag)
    #     cid = create_cid_digest(codec, hash)
    #     self._cached_cid = cid
    #
    #     return self._cached_cid

    def get_cids_for_key(self, key) -> Sequence[CID]:

        if key in self._cids_cache.keys():
            return self._cids_cache[key]

        model = self.get_serialized_data(key)
        self._cids_cache[key] = model.get_cids(hash_codec=self.hash_codec)
        return self._cids_cache[key]

    @property
    def dag(self) -> Mapping[str, Sequence[CID]]:

        if self._cached_dag is not None:
            return self._cached_dag

        dag: Dict[str, Sequence[CID]] = {}
        for key in self.get_keys():
            dag[key] = self.get_cids_for_key(key)

        self._cached_dag = dag
        return self._cached_dag

Attributes

dag: Mapping[str, Sequence[multiformats.cid.CID]] property readonly

data_size: int property readonly

data_type: str pydantic-field required

The name of the data type for this serialized value.

data_type_config: Mapping[str, Any] pydantic-field

The (optional) config for the data type for this serialized value.

hash_codec: str pydantic-field

The codec used to hash the value.

metadata: SerializationMetadata pydantic-field

Optional metadata describing aspects of the serialization used.

serialization_profile: str pydantic-field required

An identifying name for the serialization method used.

get_cids_for_key(self, key)

Source code in kiara/models/values/value.py

def get_cids_for_key(self, key) -> Sequence[CID]:

    if key in self._cids_cache.keys():
        return self._cids_cache[key]

    model = self.get_serialized_data(key)
    self._cids_cache[key] = model.get_cids(hash_codec=self.hash_codec)
    return self._cids_cache[key]

get_keys(self)

Source code in kiara/models/values/value.py

@abc.abstractmethod
def get_keys(self) -> Iterable[str]:
    pass

get_serialized_data(self, key)

Source code in kiara/models/values/value.py

@abc.abstractmethod
def get_serialized_data(self, key: str) -> SerializedChunks:
    pass

SerializedFile (SerializedPreStoreChunks) pydantic-model

Source code in kiara/models/values/value.py

class SerializedFile(SerializedPreStoreChunks):

    type: Literal["file"] = "file"
    file: str = Field(description="A path to a file containing the serialized data.")

    def get_chunks(
        self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
    ) -> Iterable[Union[str, BytesLike]]:

        if as_files is False:
            chunk = self._read_bytes_from_file(self.file)
            return [chunk]
        else:
            if as_files is True:
                return [self.file]
            else:
                if isinstance(as_files, str):
                    file = as_files
                else:
                    assert len(as_files) == 1
                    file = as_files[0]
                if os.path.exists(file):
                    raise Exception(f"Can't write to file '{file}': file exists.")
                if symlink_ok:
                    os.symlink(self.file, file)
                    return [file]
                else:
                    raise NotImplementedError()

    def get_number_of_chunks(self) -> int:
        return 1

    def _get_size(self) -> int:
        return os.path.getsize(os.path.realpath(self.file))

    def _create_cids(self, hash_codec: str) -> Sequence[CID]:
        return [self._create_cid_from_file(self.file, hash_codec=hash_codec)]

Attributes

file: str pydantic-field required

A path to a file containing the serialized data.

type: Literal['file'] pydantic-field

Methods

get_chunks(self, as_files=True, symlink_ok=True)

Retrieve the chunks belonging to this data instance.

If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of 'as_file' is also ok, otherwise copy the content.

Source code in kiara/models/values/value.py

def get_chunks(
    self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
) -> Iterable[Union[str, BytesLike]]:

    if as_files is False:
        chunk = self._read_bytes_from_file(self.file)
        return [chunk]
    else:
        if as_files is True:
            return [self.file]
        else:
            if isinstance(as_files, str):
                file = as_files
            else:
                assert len(as_files) == 1
                file = as_files[0]
            if os.path.exists(file):
                raise Exception(f"Can't write to file '{file}': file exists.")
            if symlink_ok:
                os.symlink(self.file, file)
                return [file]
            else:
                raise NotImplementedError()

get_number_of_chunks(self)

Source code in kiara/models/values/value.py

def get_number_of_chunks(self) -> int:
    return 1

SerializedFiles (SerializedPreStoreChunks) pydantic-model

Source code in kiara/models/values/value.py

class SerializedFiles(SerializedPreStoreChunks):

    type: Literal["files"] = "files"
    files: List[str] = Field(
        description="A list of strings, pointing to files containing parts of the serialized data."
    )

    def get_chunks(
        self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
    ) -> Iterable[Union[str, BytesLike]]:
        raise NotImplementedError()

    def get_number_of_chunks(self) -> int:
        return len(self.files)

    def _get_size(self) -> int:

        size = 0
        for file in self.files:
            size = size + os.path.getsize(os.path.realpath(file))
        return size

    def _create_cids(self, hash_codec: str) -> Sequence[CID]:
        return [
            self._create_cid_from_file(file, hash_codec=hash_codec)
            for file in self.files
        ]

Attributes

files: List[str] pydantic-field required

A list of strings, pointing to files containing parts of the serialized data.

type: Literal['files'] pydantic-field

Methods

get_chunks(self, as_files=True, symlink_ok=True)

Retrieve the chunks belonging to this data instance.

If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of 'as_file' is also ok, otherwise copy the content.

Source code in kiara/models/values/value.py

def get_chunks(
    self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
) -> Iterable[Union[str, BytesLike]]:
    raise NotImplementedError()

get_number_of_chunks(self)

Source code in kiara/models/values/value.py

def get_number_of_chunks(self) -> int:
    return len(self.files)

SerializedInlineJson (SerializedPreStoreChunks) pydantic-model

Source code in kiara/models/values/value.py

class SerializedInlineJson(SerializedPreStoreChunks):

    type: Literal["inline-json"] = "inline-json"
    inline_data: Any = Field(
        description="Data that will not be stored externally, but inline in the containing model. This should only contain data types that can be serialized reliably using json (scalars, etc.)."
    )
    _json_cache: Optional[bytes] = PrivateAttr(default=None)

    def as_json(self) -> bytes:
        assert self.inline_data is not None
        if self._json_cache is None:
            self._json_cache = orjson.dumps(self.inline_data)
        return self._json_cache

    def get_chunks(
        self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
    ) -> Iterable[Union[str, BytesLike]]:

        if as_files is False:
            return [self.as_json()]
        else:
            raise NotImplementedError()

    def get_number_of_chunks(self) -> int:
        return 1

    def _get_size(self) -> int:
        return len(self.as_json())

    def _create_cids(self, hash_codec: str) -> Sequence[CID]:
        return [self._create_cid_from_chunk(self.as_json(), hash_codec=hash_codec)]

Attributes

inline_data: Any pydantic-field

Data that will not be stored externally, but inline in the containing model. This should only contain data types that can be serialized reliably using json (scalars, etc.).

type: Literal['inline-json'] pydantic-field

Methods

as_json(self)

Source code in kiara/models/values/value.py

def as_json(self) -> bytes:
    assert self.inline_data is not None
    if self._json_cache is None:
        self._json_cache = orjson.dumps(self.inline_data)
    return self._json_cache

get_chunks(self, as_files=True, symlink_ok=True)

Retrieve the chunks belonging to this data instance.

If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of 'as_file' is also ok, otherwise copy the content.

Source code in kiara/models/values/value.py

def get_chunks(
    self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
) -> Iterable[Union[str, BytesLike]]:

    if as_files is False:
        return [self.as_json()]
    else:
        raise NotImplementedError()

get_number_of_chunks(self)

Source code in kiara/models/values/value.py

def get_number_of_chunks(self) -> int:
    return 1

SerializedListOfBytes (SerializedPreStoreChunks) pydantic-model

Source code in kiara/models/values/value.py

class SerializedListOfBytes(SerializedPreStoreChunks):

    type: Literal["chunks"] = "chunks"
    chunks: List[bytes] = Field(description="A list of byte arrays.")

    def get_chunks(
        self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
    ) -> Iterable[Union[str, BytesLike]]:
        if as_files is False:
            return self.chunks
        else:
            if as_files is None or as_files is True or isinstance(as_files, str):
                # means we write all the chunks into one file
                file = None if as_files is True else as_files
                path = self._store_bytes_to_file(self.chunks, file=file)
                return [path]
            else:
                assert len(as_files) == self.get_number_of_chunks()
                result = []
                for idx, chunk in enumerate(self.chunks):
                    _file = as_files[idx]
                    path = self._store_bytes_to_file([chunk], file=_file)
                    result.append(path)
                return result

    def get_number_of_chunks(self) -> int:
        return len(self.chunks)

    def _get_size(self) -> int:
        size = 0
        for chunk in self.chunks:
            size = size + len(chunk)
        return size

    def _create_cids(self, hash_codec: str) -> Sequence[CID]:
        return [
            self._create_cid_from_chunk(chunk, hash_codec=hash_codec)
            for chunk in self.chunks
        ]

Attributes

chunks: List[bytes] pydantic-field required

A list of byte arrays.

type: Literal['chunks'] pydantic-field

Methods

get_chunks(self, as_files=True, symlink_ok=True)

Retrieve the chunks belonging to this data instance.

If 'as_file' is False, return the data as bytes. If set to 'True' store it to an arbitrary location (or use an existing one), and return the path to that file. If 'as_file' is a string, write the data (bytes) into a new file using the string as path. If 'symlink_ok' is set to True, symlinking an existing file to the value of 'as_file' is also ok, otherwise copy the content.

Source code in kiara/models/values/value.py

def get_chunks(
    self, as_files: Union[bool, str, Sequence[str]] = True, symlink_ok: bool = True
) -> Iterable[Union[str, BytesLike]]:
    if as_files is False:
        return self.chunks
    else:
        if as_files is None or as_files is True or isinstance(as_files, str):
            # means we write all the chunks into one file
            file = None if as_files is True else as_files
            path = self._store_bytes_to_file(self.chunks, file=file)
            return [path]
        else:
            assert len(as_files) == self.get_number_of_chunks()
            result = []
            for idx, chunk in enumerate(self.chunks):
                _file = as_files[idx]
                path = self._store_bytes_to_file([chunk], file=_file)
                result.append(path)
            return result

get_number_of_chunks(self)

Source code in kiara/models/values/value.py

def get_number_of_chunks(self) -> int:
    return len(self.chunks)

SerializedPreStoreChunks (SerializedChunks) pydantic-model

Source code in kiara/models/values/value.py

class SerializedPreStoreChunks(SerializedChunks):

    codec: str = Field(
        description="The codec used to encode the chunks in this model. Using the [multicodecs](https://github.com/multiformats/multicodec) codec table."
    )

    def _create_cid_from_chunk(self, chunk: bytes, hash_codec: str) -> CID:

        multihash = Multihash(codec=hash_codec)
        hash = multihash.digest(chunk)
        return create_cid_digest(digest=hash, codec=self.codec)

    def _create_cid_from_file(self, file: str, hash_codec: str) -> CID:

        assert hash_codec == "sha2-256"

        hash_func = hashlib.sha256
        file_hash = hash_func()

        CHUNK_SIZE = 65536
        with open(file, "rb") as f:
            fb = f.read(CHUNK_SIZE)
            while len(fb) > 0:
                file_hash.update(fb)
                fb = f.read(CHUNK_SIZE)

        wrapped = multihash.wrap(file_hash.digest(), "sha2-256")
        return create_cid_digest(digest=wrapped, codec=self.codec)

Attributes

codec: str pydantic-field required

The codec used to encode the chunks in this model. Using the multicodecs codec table.

UnloadableData (KiaraModel) pydantic-model

A special 'marker' model, indicating that the data of value can't be loaded.

In most cases, the reason this happens is because the current kiara context is missing some value types and/or modules.

Source code in kiara/models/values/value.py

class UnloadableData(KiaraModel):
    """A special 'marker' model, indicating that the data of value can't be loaded.

    In most cases, the reason this happens is because the current kiara context is missing some value types and/or modules."""

    _kiara_model_id = "instance.unloadable_data"

    value: Value = Field(description="A reference to the value.")

    def _retrieve_id(self) -> str:
        return self.value.instance_id

    def _retrieve_data_to_hash(self) -> Any:
        return self.value.value_id.bytes

Attributes

value: Value pydantic-field required

A reference to the value.

Value (ValueDetails) pydantic-model

Source code in kiara/models/values/value.py

class Value(ValueDetails):

    _kiara_model_id = "instance.value"

    _value_data: Any = PrivateAttr(default=SpecialValue.NOT_SET)
    _serialized_data: Union[None, str, SerializedData] = PrivateAttr(default=None)
    _data_retrieved: bool = PrivateAttr(default=False)
    _data_registry: "DataRegistry" = PrivateAttr(default=None)
    _data_type: "DataType" = PrivateAttr(default=None)
    _is_stored: bool = PrivateAttr(default=False)
    _cached_properties: Optional["ValueMap"] = PrivateAttr(default=None)

    property_links: Mapping[str, uuid.UUID] = Field(
        description="Links to values that are properties of this value.",
        default_factory=dict,
    )
    destiny_backlinks: Mapping[uuid.UUID, str] = Field(
        description="Backlinks to values that this value acts as destiny/or property for.",
        default_factory=dict,
    )

    def add_property(
        self,
        value_id: Union[uuid.UUID, "Value"],
        property_path: str,
        add_origin_to_property_value: bool = True,
    ):

        value = None
        try:
            value_temp = value
            value_id = value_id.value_id  # type: ignore
            value = value_temp
        except Exception:
            # in case a Value object was provided
            pass
        finally:
            del value_temp

        if add_origin_to_property_value:
            if value is None:
                value = self._data_registry.get_value(value_id=value_id)  # type: ignore

            if value._is_stored:
                raise Exception(
                    f"Can't add property to value '{self.value_id}': referenced value '{value.value_id}' already locked, so it's not possible to add the property backlink (as requested)."
                )

        assert value is not None

        if self._is_stored:
            raise Exception(
                f"Can't add property to value '{self.value_id}': value already locked."
            )

        if property_path in self.property_links.keys():
            raise Exception(
                f"Can't add property to value '{self.value_id}': property '{property_path}' already set."
            )

        self.property_links[property_path] = value_id  # type: ignore

        if add_origin_to_property_value:
            value.add_destiny_details(
                value_id=self.value_id, destiny_alias=property_path
            )

        self._cached_properties = None

    def add_destiny_details(self, value_id: uuid.UUID, destiny_alias: str):

        if self._is_stored:
            raise Exception(
                f"Can't set destiny_refs to value '{self.value_id}': value already locked."
            )

        self.destiny_backlinks[value_id] = destiny_alias  # type: ignore

    @property
    def is_serializable(self) -> bool:

        try:
            if self._serialized_data == NO_SERIALIZATION_MARKER:
                return False
            self.serialized_data
            return True
        except Exception:
            pass

        return False

    @property
    def serialized_data(self) -> SerializedData:

        # if not self.is_set:
        #     raise Exception(f"Can't retrieve serialized data: value not set.")

        if self._serialized_data is not None:
            if isinstance(self._serialized_data, str):
                raise Exception(
                    f"Data type '{self.data_type_name}' does not support serializing: {self._serialized_data}"
                )

            return self._serialized_data

        self._serialized_data = self._data_registry.retrieve_persisted_value_details(
            self.value_id
        )
        return self._serialized_data

    @property
    def data(self) -> Any:
        if not self.is_initialized:
            raise Exception(
                f"Can't retrieve data for value '{self.value_id}': value not initialized yet. This is most likely a bug."
            )
        return self._retrieve_data()

    def _retrieve_data(self) -> Any:

        if self._value_data is not SpecialValue.NOT_SET:
            return self._value_data

        if self.value_status in [ValueStatus.NOT_SET, ValueStatus.NONE]:
            self._value_data = None
            return self._value_data
        elif self.value_status not in [ValueStatus.SET, ValueStatus.DEFAULT]:
            raise Exception(f"Invalid internal state of value '{self.value_id}'.")

        retrieved = self._data_registry.retrieve_value_data(value=self)

        if retrieved is None or isinstance(retrieved, SpecialValue):
            raise Exception(
                f"Can't set value data, invalid data type: {type(retrieved)}"
            )

        self._value_data = retrieved
        self._data_retrieved = True
        return self._value_data

    # def retrieve_load_config(self) -> Optional[LoadConfig]:
    #     return self._data_registry.retrieve_persisted_value_details(
    #         value_id=self.value_id
    #     )

    def __repr__(self):

        return f"{self.__class__.__name__}(id={self.value_id}, type={self.data_type_name}, status={self.value_status.value}, initialized={self.is_initialized} optional={self.value_schema.optional})"

    def _set_registry(self, data_registry: "DataRegistry") -> None:
        self._data_registry = data_registry

    @property
    def is_initialized(self) -> bool:
        result = not self.is_set or self._data_registry is not None
        return result

    @property
    def is_stored(self) -> bool:
        return self._is_stored

    @property
    def data_type(self) -> "DataType":

        if self._data_type is not None:
            return self._data_type

        cls = self.data_type_class.get_class()
        self._data_type = cls(**self.value_schema.type_config)
        return self._data_type

    @property
    def property_values(self) -> "ValueMap":

        if self._cached_properties is not None:
            return self._cached_properties

        self._cached_properties = self._data_registry.load_values(self.property_links)
        return self._cached_properties

    @property
    def property_names(self) -> Iterable[str]:
        return self.property_links.keys()

    def get_property_value(self, property_key) -> "Value":

        if property_key not in self.property_links.keys():
            raise Exception(
                f"Value '{self.value_id}' has no property with key '{property_key}."
            )

        return self._data_registry.get_value(self.property_links[property_key])

    def get_property_data(self, property_key: str) -> Any:

        return self.get_property_value(property_key=property_key).data

    def create_renderable(self, **render_config: Any) -> RenderableType:

        from kiara.utils.output import extract_renderable

        show_pedigree = render_config.get("show_pedigree", False)
        show_lineage = render_config.get("show_lineage", False)
        show_properties = render_config.get("show_properties", False)
        show_destinies = render_config.get("show_destinies", False)
        show_destiny_backlinks = render_config.get("show_destiny_backlinks", False)
        show_data = render_config.get("show_data_preview", False)
        show_serialized = render_config.get("show_serialized", False)

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("Key", style="i")
        table.add_column("Value")

        table.add_row("value_id", str(self.value_id))
        if hasattr(self, "aliases"):
            if not self.aliases:  # type: ignore
                aliases_str = "-- n/a --"
            else:
                aliases_str = ", ".join(self.aliases)  # type: ignore
            table.add_row("aliases", aliases_str)
        table.add_row("kiara_id", str(self.kiara_id))
        table.add_row("", "")
        table.add_row("", Rule())
        for k in sorted(self.__fields__.keys()):

            if k in ["serialized", "value_id", "aliases", "kiara_id"]:
                continue

            attr = getattr(self, k)
            if k in ["pedigree_output_name", "pedigree"]:
                continue

            elif k == "value_status":
                v = f"[i]-- {attr.value} --[/i]"
            elif k == "value_size":
                v = format_size(attr)
            else:
                v = extract_renderable(attr)

            table.add_row(k, v)

        if (
            show_pedigree
            or show_lineage
            or show_serialized
            or show_properties
            or show_destinies
            or show_destiny_backlinks
        ):
            table.add_row("", "")
            table.add_row("", Rule())
            table.add_row("", "")

        if show_pedigree:
            pedigree = getattr(self, "pedigree")

            if pedigree == ORPHAN:
                v = "[i]-- external data --[/i]"
                pedigree_output_name: Optional[Any] = None
            else:
                v = extract_renderable(pedigree)
                pedigree_output_name = getattr(self, "pedigree_output_name")

            row = ["pedigree", v]
            table.add_row(*row)
            if pedigree_output_name:
                row = ["pedigree_output_name", pedigree_output_name]
                table.add_row(*row)

        if show_lineage:
            from kiara.models.values.lineage import ValueLineage

            vl = ValueLineage(kiara=self._data_registry._kiara, value=self)
            table.add_row("lineage", vl.create_renderable(include_ids=True))

        if show_serialized:
            serialized = self._data_registry.retrieve_persisted_value_details(
                self.value_id
            )
            table.add_row("serialized", serialized.create_renderable())

        if show_properties:
            if not self.property_links:
                table.add_row("properties", "{}")
            else:
                properties = self._data_registry.load_values(self.property_links)
                pr = properties.create_renderable(show_header=False)
                table.add_row("properties", pr)

        if hasattr(self, "destiny_links") and show_destinies:
            if not self.destiny_links:  # type: ignore
                table.add_row("destinies", "{}")
            else:
                destinies = self._data_registry.load_values(self.destiny_links)  # type: ignore
                dr = destinies.create_renderable(show_header=False)
                table.add_row("destinies", dr)

        if show_destiny_backlinks:
            if not self.destiny_backlinks:
                table.add_row("destiny backlinks", "{}")
            else:
                destiny_items: List[Any] = []
                for v_id, alias in self.destiny_backlinks.items():
                    destiny_items.append(Rule())
                    destiny_items.append(
                        f"[b]Value: [i]{v_id}[/i] (destiny alias: {alias})[/b]"
                    )
                    rendered = self._data_registry.pretty_print_data(
                        value_id=v_id, **render_config
                    )
                    destiny_items.append(rendered)
                table.add_row("destiny backlinks", Group(*destiny_items))

        if show_data:
            rendered = self._data_registry.pretty_print_data(
                self.value_id, target_type="terminal_renderable"
            )
            table.add_row("", "")
            table.add_row("", Rule())
            table.add_row("data preview", rendered)

        return table

Attributes

data: Any property readonly

data_type: DataType property readonly

destiny_backlinks: Mapping[uuid.UUID, str] pydantic-field

Backlinks to values that this value acts as destiny/or property for.

is_initialized: bool property readonly

is_serializable: bool property readonly

is_stored: bool property readonly

property_links: Mapping[str, uuid.UUID] pydantic-field

Links to values that are properties of this value.

property_names: Iterable[str] property readonly

property_values: ValueMap property readonly

serialized_data: SerializedData property readonly

add_destiny_details(self, value_id, destiny_alias)

Source code in kiara/models/values/value.py

def add_destiny_details(self, value_id: uuid.UUID, destiny_alias: str):

    if self._is_stored:
        raise Exception(
            f"Can't set destiny_refs to value '{self.value_id}': value already locked."
        )

    self.destiny_backlinks[value_id] = destiny_alias  # type: ignore

add_property(self, value_id, property_path, add_origin_to_property_value=True)

Source code in kiara/models/values/value.py

def add_property(
    self,
    value_id: Union[uuid.UUID, "Value"],
    property_path: str,
    add_origin_to_property_value: bool = True,
):

    value = None
    try:
        value_temp = value
        value_id = value_id.value_id  # type: ignore
        value = value_temp
    except Exception:
        # in case a Value object was provided
        pass
    finally:
        del value_temp

    if add_origin_to_property_value:
        if value is None:
            value = self._data_registry.get_value(value_id=value_id)  # type: ignore

        if value._is_stored:
            raise Exception(
                f"Can't add property to value '{self.value_id}': referenced value '{value.value_id}' already locked, so it's not possible to add the property backlink (as requested)."
            )

    assert value is not None

    if self._is_stored:
        raise Exception(
            f"Can't add property to value '{self.value_id}': value already locked."
        )

    if property_path in self.property_links.keys():
        raise Exception(
            f"Can't add property to value '{self.value_id}': property '{property_path}' already set."
        )

    self.property_links[property_path] = value_id  # type: ignore

    if add_origin_to_property_value:
        value.add_destiny_details(
            value_id=self.value_id, destiny_alias=property_path
        )

    self._cached_properties = None

create_renderable(self, **render_config)

Source code in kiara/models/values/value.py

def create_renderable(self, **render_config: Any) -> RenderableType:

    from kiara.utils.output import extract_renderable

    show_pedigree = render_config.get("show_pedigree", False)
    show_lineage = render_config.get("show_lineage", False)
    show_properties = render_config.get("show_properties", False)
    show_destinies = render_config.get("show_destinies", False)
    show_destiny_backlinks = render_config.get("show_destiny_backlinks", False)
    show_data = render_config.get("show_data_preview", False)
    show_serialized = render_config.get("show_serialized", False)

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("Key", style="i")
    table.add_column("Value")

    table.add_row("value_id", str(self.value_id))
    if hasattr(self, "aliases"):
        if not self.aliases:  # type: ignore
            aliases_str = "-- n/a --"
        else:
            aliases_str = ", ".join(self.aliases)  # type: ignore
        table.add_row("aliases", aliases_str)
    table.add_row("kiara_id", str(self.kiara_id))
    table.add_row("", "")
    table.add_row("", Rule())
    for k in sorted(self.__fields__.keys()):

        if k in ["serialized", "value_id", "aliases", "kiara_id"]:
            continue

        attr = getattr(self, k)
        if k in ["pedigree_output_name", "pedigree"]:
            continue

        elif k == "value_status":
            v = f"[i]-- {attr.value} --[/i]"
        elif k == "value_size":
            v = format_size(attr)
        else:
            v = extract_renderable(attr)

        table.add_row(k, v)

    if (
        show_pedigree
        or show_lineage
        or show_serialized
        or show_properties
        or show_destinies
        or show_destiny_backlinks
    ):
        table.add_row("", "")
        table.add_row("", Rule())
        table.add_row("", "")

    if show_pedigree:
        pedigree = getattr(self, "pedigree")

        if pedigree == ORPHAN:
            v = "[i]-- external data --[/i]"
            pedigree_output_name: Optional[Any] = None
        else:
            v = extract_renderable(pedigree)
            pedigree_output_name = getattr(self, "pedigree_output_name")

        row = ["pedigree", v]
        table.add_row(*row)
        if pedigree_output_name:
            row = ["pedigree_output_name", pedigree_output_name]
            table.add_row(*row)

    if show_lineage:
        from kiara.models.values.lineage import ValueLineage

        vl = ValueLineage(kiara=self._data_registry._kiara, value=self)
        table.add_row("lineage", vl.create_renderable(include_ids=True))

    if show_serialized:
        serialized = self._data_registry.retrieve_persisted_value_details(
            self.value_id
        )
        table.add_row("serialized", serialized.create_renderable())

    if show_properties:
        if not self.property_links:
            table.add_row("properties", "{}")
        else:
            properties = self._data_registry.load_values(self.property_links)
            pr = properties.create_renderable(show_header=False)
            table.add_row("properties", pr)

    if hasattr(self, "destiny_links") and show_destinies:
        if not self.destiny_links:  # type: ignore
            table.add_row("destinies", "{}")
        else:
            destinies = self._data_registry.load_values(self.destiny_links)  # type: ignore
            dr = destinies.create_renderable(show_header=False)
            table.add_row("destinies", dr)

    if show_destiny_backlinks:
        if not self.destiny_backlinks:
            table.add_row("destiny backlinks", "{}")
        else:
            destiny_items: List[Any] = []
            for v_id, alias in self.destiny_backlinks.items():
                destiny_items.append(Rule())
                destiny_items.append(
                    f"[b]Value: [i]{v_id}[/i] (destiny alias: {alias})[/b]"
                )
                rendered = self._data_registry.pretty_print_data(
                    value_id=v_id, **render_config
                )
                destiny_items.append(rendered)
            table.add_row("destiny backlinks", Group(*destiny_items))

    if show_data:
        rendered = self._data_registry.pretty_print_data(
            self.value_id, target_type="terminal_renderable"
        )
        table.add_row("", "")
        table.add_row("", Rule())
        table.add_row("data preview", rendered)

    return table

get_property_data(self, property_key)

Source code in kiara/models/values/value.py

def get_property_data(self, property_key: str) -> Any:

    return self.get_property_value(property_key=property_key).data

get_property_value(self, property_key)

Source code in kiara/models/values/value.py

def get_property_value(self, property_key) -> "Value":

    if property_key not in self.property_links.keys():
        raise Exception(
            f"Value '{self.value_id}' has no property with key '{property_key}."
        )

    return self._data_registry.get_value(self.property_links[property_key])

ValueDetails (KiaraModel) pydantic-model

A wrapper class that manages and retieves value data and its details.

Source code in kiara/models/values/value.py

class ValueDetails(KiaraModel):
    """A wrapper class that manages and retieves value data and its details."""

    _kiara_model_id = "instance.value_details"

    value_id: uuid.UUID = Field(description="The id of the value.")

    kiara_id: uuid.UUID = Field(
        description="The id of the kiara context this value belongs to."
    )

    value_schema: ValueSchema = Field(
        description="The schema that was used for this Value."
    )

    value_status: ValueStatus = Field(description="The set/unset status of this value.")
    value_size: int = Field(description="The size of this value, in bytes.")
    value_hash: str = Field(description="The hash of this value.")
    pedigree: ValuePedigree = Field(
        description="Information about the module and inputs that went into creating this value."
    )
    pedigree_output_name: str = Field(
        description="The output name that produced this value (using the manifest inside the pedigree)."
    )
    data_type_class: PythonClass = Field(
        description="The python class that is associtated with this model."
    )

    def _retrieve_id(self) -> str:
        return str(self.value_id)

    def _retrieve_data_to_hash(self) -> Any:
        return {
            "value_type": self.value_schema.type,
            "value_hash": self.value_hash,
            "value_size": self.value_size,
        }

    @property
    def data_type_name(self) -> str:
        return self.value_schema.type

    @property
    def data_type_config(self) -> Mapping[str, Any]:
        return self.value_schema.type_config

    @property
    def is_optional(self) -> bool:
        return self.value_schema.optional

    @property
    def is_valid(self) -> bool:
        """Check whether the current value is valid"""

        if self.is_optional:
            return True
        else:
            return self.value_status == ValueStatus.SET

    @property
    def is_set(self) -> bool:
        return self.value_status in [ValueStatus.SET, ValueStatus.DEFAULT]

    @property
    def value_status_string(self) -> str:
        """Print a human readable short description of this values status."""

        if self.value_status == ValueStatus.DEFAULT:
            return "set (default)"
        elif self.value_status == ValueStatus.SET:
            return "set"
        elif self.value_status == ValueStatus.NONE:
            result = "no value"
        elif self.value_status == ValueStatus.NOT_SET:
            result = "not set"
        else:
            raise Exception(
                f"Invalid internal status of value '{self.value_id}'. This is most likely a bug."
            )

        if self.is_optional:
            result = f"{result} (not required)"
        return result

    def __repr__(self):

        return f"{self.__class__.__name__}(id={self.value_id}, type={self.data_type_name}, status={self.value_status.value})"

    def __str__(self):

        return self.__repr__()

Attributes

data_type_class: PythonClass pydantic-field required

The python class that is associtated with this model.

data_type_config: Mapping[str, Any] property readonly

data_type_name: str property readonly

is_optional: bool property readonly

is_set: bool property readonly

is_valid: bool property readonly

Check whether the current value is valid

kiara_id: UUID pydantic-field required

The id of the kiara context this value belongs to.

pedigree: ValuePedigree pydantic-field required

Information about the module and inputs that went into creating this value.

pedigree_output_name: str pydantic-field required

The output name that produced this value (using the manifest inside the pedigree).

value_hash: str pydantic-field required

The hash of this value.

value_id: UUID pydantic-field required

The id of the value.

value_schema: ValueSchema pydantic-field required

The schema that was used for this Value.

value_size: int pydantic-field required

The size of this value, in bytes.

value_status: ValueStatus pydantic-field required

The set/unset status of this value.

value_status_string: str property readonly

Print a human readable short description of this values status.

ValueMap (KiaraModel, MutableMapping, Generic) pydantic-model

Source code in kiara/models/values/value.py

class ValueMap(KiaraModel, MutableMapping[str, Value]):  # type: ignore

    values_schema: Dict[str, ValueSchema] = Field(
        description="The schemas for all the values in this set."
    )

    @property
    def field_names(self) -> Iterable[str]:
        return sorted(self.values_schema.keys())

    @abc.abstractmethod
    def get_value_obj(self, field_name: str) -> Value:
        pass

    @property
    def all_items_valid(self) -> bool:
        for field_name in self.values_schema.keys():
            item = self.get_value_obj(field_name)
            if not item.is_valid:
                return False
        return True

    def _retrieve_data_to_hash(self) -> Any:
        return {
            k: self.get_value_obj(k).instance_cid for k in self.values_schema.keys()
        }

    def check_invalid(self) -> Dict[str, str]:
        """Check whether the value set is invalid, if it is, return a description of what's wrong."""

        invalid: Dict[str, str] = {}
        for field_name in self.values_schema.keys():
            item = self.get_value_obj(field_name)
            field_schema = self.values_schema[field_name]
            if not field_schema.optional:
                msg: Optional[str] = None
                if not item.value_status == ValueStatus.SET:

                    item_schema = self.values_schema[field_name]
                    if item_schema.is_required():

                        if not item.is_set:
                            msg = "not set"
                        elif item.value_status == ValueStatus.NONE:
                            msg = "no value"
                if msg:
                    invalid[field_name] = msg

        return invalid

    def get_value_data_for_fields(
        self, *field_names: str, raise_exception_when_unset: bool = False
    ) -> Dict[str, Any]:
        """Return the data for a one or several fields of this ValueMap.

        If a value is unset, by default 'None' is returned for it. Unless 'raise_exception_when_unset' is set to 'True',
        in which case an Exception will be raised (obviously).
        """

        if raise_exception_when_unset:
            unset: List[str] = []
            for k in field_names:
                v = self.get_value_obj(k)
                if not v.is_set:
                    if raise_exception_when_unset:
                        unset.append(k)
            if unset:
                raise Exception(
                    f"Can't get data for fields, one or several of the requested fields are not set yet: {', '.join(unset)}."
                )

        result: Dict[str, Any] = {}
        for k in field_names:
            v = self.get_value_obj(k)
            if not v.is_set:
                result[k] = None
            else:
                result[k] = v.data
        return result

    def get_value_data(
        self, field_name: str, raise_exception_when_unset: bool = False
    ) -> Any:
        return self.get_value_data_for_fields(
            field_name, raise_exception_when_unset=raise_exception_when_unset
        )[field_name]

    def get_all_value_ids(self) -> Dict[str, uuid.UUID]:
        return {k: self.get_value_obj(k).value_id for k in self.field_names}

    def get_all_value_data(
        self, raise_exception_when_unset: bool = False
    ) -> Dict[str, Any]:
        return self.get_value_data_for_fields(
            *self.field_names,
            raise_exception_when_unset=raise_exception_when_unset,
        )

    def set_values(self, **values) -> None:

        for k, v in values.items():
            self.set_value(k, v)

    def set_value(self, field_name: str, data: Any) -> None:
        raise Exception(
            f"The value set implementation '{self.__class__.__name__}' is read-only, and does not support the setting or changing of values."
        )

    def __getitem__(self, item: str) -> Value:

        return self.get_value_obj(item)

    def __setitem__(self, key: str, value):

        raise NotImplementedError()
        # self.set_value(key, value)

    def __delitem__(self, key: str):

        raise Exception(f"Removing items not supported: {key}")

    def __iter__(self):
        return iter(self.field_names)

    def __len__(self):
        return len(list(self.values_schema))

    def __repr__(self):
        return f"{self.__class__.__name__}(field_names={self.field_names})"

    def __str__(self):
        return self.__repr__()

    def create_invalid_renderable(self, **config) -> Optional[RenderableType]:

        inv = self.check_invalid()
        if not inv:
            return None

        table = Table(show_header=False, box=box.SIMPLE)
        table.add_column("field name", style="i")
        table.add_column("details", style="b red")

        for field, err in inv.items():
            table.add_row(field, err)

        return table

    def create_renderable(self, **config: Any) -> RenderableType:

        render_value_data = config.get("render_value_data", True)
        field_title = config.get("field_title", "field")
        value_title = config.get("value_title", "value")
        show_header = config.get("show_header", True)
        show_type = config.get("show_data_type", False)

        table = Table(show_lines=False, show_header=show_header, box=box.SIMPLE)
        table.add_column(field_title, style="b")
        if show_type:
            table.add_column("data_type")
        table.add_column(value_title, style="i")

        for field_name in self.field_names:

            value = self.get_value_obj(field_name=field_name)
            if render_value_data:
                rendered = value._data_registry.pretty_print_data(
                    value_id=value.value_id, target_type="terminal_renderable", **config
                )
            else:
                rendered = value.create_renderable(**config)

            if show_type:
                table.add_row(field_name, value.value_schema.type, rendered)
            else:
                table.add_row(field_name, rendered)

        return table

Attributes

all_items_valid: bool property readonly

field_names: Iterable[str] property readonly

values_schema: Dict[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

The schemas for all the values in this set.

Methods

check_invalid(self)

Check whether the value set is invalid, if it is, return a description of what's wrong.

Source code in kiara/models/values/value.py

def check_invalid(self) -> Dict[str, str]:
    """Check whether the value set is invalid, if it is, return a description of what's wrong."""

    invalid: Dict[str, str] = {}
    for field_name in self.values_schema.keys():
        item = self.get_value_obj(field_name)
        field_schema = self.values_schema[field_name]
        if not field_schema.optional:
            msg: Optional[str] = None
            if not item.value_status == ValueStatus.SET:

                item_schema = self.values_schema[field_name]
                if item_schema.is_required():

                    if not item.is_set:
                        msg = "not set"
                    elif item.value_status == ValueStatus.NONE:
                        msg = "no value"
            if msg:
                invalid[field_name] = msg

    return invalid

create_invalid_renderable(self, **config)

Source code in kiara/models/values/value.py

def create_invalid_renderable(self, **config) -> Optional[RenderableType]:

    inv = self.check_invalid()
    if not inv:
        return None

    table = Table(show_header=False, box=box.SIMPLE)
    table.add_column("field name", style="i")
    table.add_column("details", style="b red")

    for field, err in inv.items():
        table.add_row(field, err)

    return table

create_renderable(self, **config)

Source code in kiara/models/values/value.py

def create_renderable(self, **config: Any) -> RenderableType:

    render_value_data = config.get("render_value_data", True)
    field_title = config.get("field_title", "field")
    value_title = config.get("value_title", "value")
    show_header = config.get("show_header", True)
    show_type = config.get("show_data_type", False)

    table = Table(show_lines=False, show_header=show_header, box=box.SIMPLE)
    table.add_column(field_title, style="b")
    if show_type:
        table.add_column("data_type")
    table.add_column(value_title, style="i")

    for field_name in self.field_names:

        value = self.get_value_obj(field_name=field_name)
        if render_value_data:
            rendered = value._data_registry.pretty_print_data(
                value_id=value.value_id, target_type="terminal_renderable", **config
            )
        else:
            rendered = value.create_renderable(**config)

        if show_type:
            table.add_row(field_name, value.value_schema.type, rendered)
        else:
            table.add_row(field_name, rendered)

    return table

get_all_value_data(self, raise_exception_when_unset=False)

Source code in kiara/models/values/value.py

def get_all_value_data(
    self, raise_exception_when_unset: bool = False
) -> Dict[str, Any]:
    return self.get_value_data_for_fields(
        *self.field_names,
        raise_exception_when_unset=raise_exception_when_unset,
    )

get_all_value_ids(self)

Source code in kiara/models/values/value.py

def get_all_value_ids(self) -> Dict[str, uuid.UUID]:
    return {k: self.get_value_obj(k).value_id for k in self.field_names}

get_value_data(self, field_name, raise_exception_when_unset=False)

Source code in kiara/models/values/value.py

def get_value_data(
    self, field_name: str, raise_exception_when_unset: bool = False
) -> Any:
    return self.get_value_data_for_fields(
        field_name, raise_exception_when_unset=raise_exception_when_unset
    )[field_name]

get_value_data_for_fields(self, *field_names, *, raise_exception_when_unset=False)

Return the data for a one or several fields of this ValueMap.

If a value is unset, by default 'None' is returned for it. Unless 'raise_exception_when_unset' is set to 'True', in which case an Exception will be raised (obviously).

Source code in kiara/models/values/value.py

def get_value_data_for_fields(
    self, *field_names: str, raise_exception_when_unset: bool = False
) -> Dict[str, Any]:
    """Return the data for a one or several fields of this ValueMap.

    If a value is unset, by default 'None' is returned for it. Unless 'raise_exception_when_unset' is set to 'True',
    in which case an Exception will be raised (obviously).
    """

    if raise_exception_when_unset:
        unset: List[str] = []
        for k in field_names:
            v = self.get_value_obj(k)
            if not v.is_set:
                if raise_exception_when_unset:
                    unset.append(k)
        if unset:
            raise Exception(
                f"Can't get data for fields, one or several of the requested fields are not set yet: {', '.join(unset)}."
            )

    result: Dict[str, Any] = {}
    for k in field_names:
        v = self.get_value_obj(k)
        if not v.is_set:
            result[k] = None
        else:
            result[k] = v.data
    return result

get_value_obj(self, field_name)

Source code in kiara/models/values/value.py

@abc.abstractmethod
def get_value_obj(self, field_name: str) -> Value:
    pass

set_value(self, field_name, data)

Source code in kiara/models/values/value.py

def set_value(self, field_name: str, data: Any) -> None:
    raise Exception(
        f"The value set implementation '{self.__class__.__name__}' is read-only, and does not support the setting or changing of values."
    )

set_values(self, **values)

Source code in kiara/models/values/value.py

def set_values(self, **values) -> None:

    for k, v in values.items():
        self.set_value(k, v)

ValueMapReadOnly (ValueMap) pydantic-model

Source code in kiara/models/values/value.py

class ValueMapReadOnly(ValueMap):  # type: ignore

    _kiara_model_id = "instance.value_map.readonly"

    @classmethod
    def create_from_ids(cls, data_registry: "DataRegistry", **value_ids: uuid.UUID):

        values = {k: data_registry.get_value(v) for k, v in value_ids.items()}
        return ValueMapReadOnly.construct(value_items=values)

    value_items: Dict[str, Value] = Field(
        description="The values contained in this set."
    )

    def get_value_obj(self, field_name: str) -> Value:

        if field_name not in self.value_items.keys():
            raise KeyError(
                f"Field '{field_name}' not available in value set. Available fields: {', '.join(self.field_names)}"
            )
        return self.value_items[field_name]

Attributes

value_items: Dict[str, kiara.models.values.value.Value] pydantic-field required

The values contained in this set.

create_from_ids(data_registry, **value_ids) classmethod

Source code in kiara/models/values/value.py

@classmethod
def create_from_ids(cls, data_registry: "DataRegistry", **value_ids: uuid.UUID):

    values = {k: data_registry.get_value(v) for k, v in value_ids.items()}
    return ValueMapReadOnly.construct(value_items=values)

get_value_obj(self, field_name)

Source code in kiara/models/values/value.py

def get_value_obj(self, field_name: str) -> Value:

    if field_name not in self.value_items.keys():
        raise KeyError(
            f"Field '{field_name}' not available in value set. Available fields: {', '.join(self.field_names)}"
        )
    return self.value_items[field_name]

ValueMapWritable (ValueMap) pydantic-model

Source code in kiara/models/values/value.py

class ValueMapWritable(ValueMap):  # type: ignore

    _kiara_model_id = "instance.value_map.writeable"

    @classmethod
    def create_from_schema(
        cls, kiara: "Kiara", schema: Mapping[str, ValueSchema], pedigree: ValuePedigree
    ) -> "ValueMapWritable":

        v = ValueMapWritable(values_schema=schema, pedigree=pedigree)
        v._data_registry = kiara.data_registry
        return v

    value_items: Dict[str, Value] = Field(
        description="The values contained in this set.", default_factory=dict
    )
    pedigree: ValuePedigree = Field(
        description="The pedigree to add to all of the result values."
    )

    _values_uncommitted: Dict[str, Any] = PrivateAttr(default_factory=dict)
    _data_registry: "DataRegistry" = PrivateAttr(default=None)
    _auto_commit: bool = PrivateAttr(default=True)

    def get_value_obj(self, field_name: str) -> Value:
        """Retrieve the value object for the specified field.

        This class only creates the actual value object the first time it is requested, because there is a potential
        cost to assembling it, and it might not be needed ever.
        """

        if field_name not in self.values_schema.keys():
            raise Exception(
                f"Can't set data for field '{field_name}': field not valid, valid field names: {', '.join(self.field_names)}."
            )

        if field_name in self.value_items.keys():
            return self.value_items[field_name]
        elif field_name not in self._values_uncommitted.keys():
            raise Exception(
                f"Can't retrieve value for field '{field_name}': value not set (yet)."
            )

        schema = self.values_schema[field_name]
        value_data = self._values_uncommitted[field_name]
        if isinstance(value_data, Value):
            value = value_data
        elif isinstance(value_data, uuid.UUID):
            value = self._data_registry.get_value(value_data)
        else:
            value = self._data_registry.register_data(
                data=value_data,
                schema=schema,
                pedigree=self.pedigree,
                pedigree_output_name=field_name,
                reuse_existing=False,
            )

        self._values_uncommitted.pop(field_name)
        self.value_items[field_name] = value
        return self.value_items[field_name]

    def sync_values(self):

        for field_name in self.field_names:
            self.get_value_obj(field_name)

        invalid = self.check_invalid()
        if invalid:
            if is_debug():
                import traceback

                traceback.print_stack()
            raise InvalidValuesException(invalid_values=invalid)

    def set_value(self, field_name: str, data: Any) -> None:
        """Set the value for the specified field."""

        if field_name not in self.field_names:
            raise Exception(
                f"Can't set data for field '{field_name}': field not valid, valid field names: {', '.join(self.field_names)}."
            )
        if self.value_items.get(field_name, False):
            raise Exception(
                f"Can't set data for field '{field_name}': field already committed."
            )
        if self._values_uncommitted.get(field_name, None) is not None:
            raise Exception(
                f"Can't set data for field '{field_name}': field already set."
            )

        self._values_uncommitted[field_name] = data
        if self._auto_commit:
            self.get_value_obj(field_name=field_name)

Attributes

pedigree: ValuePedigree pydantic-field required

The pedigree to add to all of the result values.

value_items: Dict[str, kiara.models.values.value.Value] pydantic-field

The values contained in this set.

Methods

create_from_schema(kiara, schema, pedigree) classmethod

Source code in kiara/models/values/value.py

@classmethod
def create_from_schema(
    cls, kiara: "Kiara", schema: Mapping[str, ValueSchema], pedigree: ValuePedigree
) -> "ValueMapWritable":

    v = ValueMapWritable(values_schema=schema, pedigree=pedigree)
    v._data_registry = kiara.data_registry
    return v

get_value_obj(self, field_name)

Retrieve the value object for the specified field.

This class only creates the actual value object the first time it is requested, because there is a potential cost to assembling it, and it might not be needed ever.

Source code in kiara/models/values/value.py

def get_value_obj(self, field_name: str) -> Value:
    """Retrieve the value object for the specified field.

    This class only creates the actual value object the first time it is requested, because there is a potential
    cost to assembling it, and it might not be needed ever.
    """

    if field_name not in self.values_schema.keys():
        raise Exception(
            f"Can't set data for field '{field_name}': field not valid, valid field names: {', '.join(self.field_names)}."
        )

    if field_name in self.value_items.keys():
        return self.value_items[field_name]
    elif field_name not in self._values_uncommitted.keys():
        raise Exception(
            f"Can't retrieve value for field '{field_name}': value not set (yet)."
        )

    schema = self.values_schema[field_name]
    value_data = self._values_uncommitted[field_name]
    if isinstance(value_data, Value):
        value = value_data
    elif isinstance(value_data, uuid.UUID):
        value = self._data_registry.get_value(value_data)
    else:
        value = self._data_registry.register_data(
            data=value_data,
            schema=schema,
            pedigree=self.pedigree,
            pedigree_output_name=field_name,
            reuse_existing=False,
        )

    self._values_uncommitted.pop(field_name)
    self.value_items[field_name] = value
    return self.value_items[field_name]

set_value(self, field_name, data)

Set the value for the specified field.

Source code in kiara/models/values/value.py

def set_value(self, field_name: str, data: Any) -> None:
    """Set the value for the specified field."""

    if field_name not in self.field_names:
        raise Exception(
            f"Can't set data for field '{field_name}': field not valid, valid field names: {', '.join(self.field_names)}."
        )
    if self.value_items.get(field_name, False):
        raise Exception(
            f"Can't set data for field '{field_name}': field already committed."
        )
    if self._values_uncommitted.get(field_name, None) is not None:
        raise Exception(
            f"Can't set data for field '{field_name}': field already set."
        )

    self._values_uncommitted[field_name] = data
    if self._auto_commit:
        self.get_value_obj(field_name=field_name)

sync_values(self)

Source code in kiara/models/values/value.py

def sync_values(self):

    for field_name in self.field_names:
        self.get_value_obj(field_name)

    invalid = self.check_invalid()
    if invalid:
        if is_debug():
            import traceback

            traceback.print_stack()
        raise InvalidValuesException(invalid_values=invalid)

ValuePedigree (InputsManifest) pydantic-model

Source code in kiara/models/values/value.py

class ValuePedigree(InputsManifest):

    _kiara_model_id = "instance.value_pedigree"

    kiara_id: uuid.UUID = Field(
        description="The id of the kiara context a value was created in."
    )
    environments: Dict[str, str] = Field(
        description="References to the runtime environment details a value was created in."
    )

    def _retrieve_data_to_hash(self) -> Any:
        return {
            "manifest": self.manifest_cid,
            "inputs": self.inputs_cid,
            "environments": self.environments,
        }

    def __repr__(self):
        return f"ValuePedigree(module_type={self.module_type}, inputs=[{', '.join(self.inputs.keys())}], instance_id={self.instance_id})"

    def __str__(self):
        return self.__repr__()

Attributes

environments: Dict[str, str] pydantic-field required

References to the runtime environment details a value was created in.

kiara_id: UUID pydantic-field required

The id of the kiara context a value was created in.

value_metadata special

Classes

MetadataTypeClassesInfo (TypeInfoModelGroup) pydantic-model

Source code in kiara/models/values/value_metadata/__init__.py

class MetadataTypeClassesInfo(TypeInfoModelGroup):

    _kiara_model_id = "info.metadata_types"

    @classmethod
    def base_info_class(cls) -> Type[TypeInfo]:
        return MetadataTypeInfo

    type_name: Literal["value_metadata"] = "value_metadata"
    type_infos: Mapping[str, MetadataTypeInfo] = Field(
        description="The value metadata info instances for each type."
    )

Attributes

type_infos: Mapping[str, kiara.models.values.value_metadata.MetadataTypeInfo] pydantic-field required

The value metadata info instances for each type.

type_name: Literal['value_metadata'] pydantic-field

base_info_class() classmethod

Source code in kiara/models/values/value_metadata/__init__.py

@classmethod
def base_info_class(cls) -> Type[TypeInfo]:
    return MetadataTypeInfo

MetadataTypeInfo (TypeInfo) pydantic-model

Source code in kiara/models/values/value_metadata/__init__.py

class MetadataTypeInfo(TypeInfo):

    _kiara_model_id = "info.metadata_type"

    @classmethod
    def create_from_type_class(
        self, type_cls: Type[ValueMetadata]
    ) -> "MetadataTypeInfo":

        authors_md = AuthorsMetadataModel.from_class(type_cls)
        doc = DocumentationMetadataModel.from_class_doc(type_cls)
        python_class = PythonClass.from_class(type_cls)
        properties_md = ContextMetadataModel.from_class(type_cls)
        type_name = type_cls._metadata_key  # type: ignore
        schema = type_cls.schema()

        return MetadataTypeInfo.construct(
            type_name=type_name,
            documentation=doc,
            authors=authors_md,
            context=properties_md,
            python_class=python_class,
            metadata_schema=schema,
        )

    @classmethod
    def base_class(self) -> Type[ValueMetadata]:
        return ValueMetadata

    @classmethod
    def category_name(cls) -> str:
        return "value_metadata"

    metadata_schema: Dict[str, Any] = Field(
        description="The (json) schema for this metadata value."
    )

    def create_renderable(self, **config: Any) -> RenderableType:

        include_doc = config.get("include_doc", True)
        include_schema = config.get("include_schema", True)

        table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
        table.add_column("property", style="i")
        table.add_column("value")

        if include_doc:
            table.add_row(
                "Documentation",
                Panel(self.documentation.create_renderable(), box=box.SIMPLE),
            )
        table.add_row("Author(s)", self.authors.create_renderable())
        table.add_row("Context", self.context.create_renderable())

        if hasattr(self, "python_class"):
            table.add_row("Python class", self.python_class.create_renderable())

        if include_schema:
            schema = Syntax(
                orjson_dumps(self.metadata_schema, option=orjson.OPT_INDENT_2),
                "json",
                background_color="default",
            )
            table.add_row("metadata_schema", schema)

        return table

Attributes

metadata_schema: Dict[str, Any] pydantic-field required

The (json) schema for this metadata value.

base_class() classmethod

Source code in kiara/models/values/value_metadata/__init__.py

@classmethod
def base_class(self) -> Type[ValueMetadata]:
    return ValueMetadata

category_name() classmethod

Source code in kiara/models/values/value_metadata/__init__.py

@classmethod
def category_name(cls) -> str:
    return "value_metadata"

create_from_type_class(type_cls) classmethod

Source code in kiara/models/values/value_metadata/__init__.py

@classmethod
def create_from_type_class(
    self, type_cls: Type[ValueMetadata]
) -> "MetadataTypeInfo":

    authors_md = AuthorsMetadataModel.from_class(type_cls)
    doc = DocumentationMetadataModel.from_class_doc(type_cls)
    python_class = PythonClass.from_class(type_cls)
    properties_md = ContextMetadataModel.from_class(type_cls)
    type_name = type_cls._metadata_key  # type: ignore
    schema = type_cls.schema()

    return MetadataTypeInfo.construct(
        type_name=type_name,
        documentation=doc,
        authors=authors_md,
        context=properties_md,
        python_class=python_class,
        metadata_schema=schema,
    )

create_renderable(self, **config)

Source code in kiara/models/values/value_metadata/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:

    include_doc = config.get("include_doc", True)
    include_schema = config.get("include_schema", True)

    table = Table(box=box.SIMPLE, show_header=False, padding=(0, 0, 0, 0))
    table.add_column("property", style="i")
    table.add_column("value")

    if include_doc:
        table.add_row(
            "Documentation",
            Panel(self.documentation.create_renderable(), box=box.SIMPLE),
        )
    table.add_row("Author(s)", self.authors.create_renderable())
    table.add_row("Context", self.context.create_renderable())

    if hasattr(self, "python_class"):
        table.add_row("Python class", self.python_class.create_renderable())

    if include_schema:
        schema = Syntax(
            orjson_dumps(self.metadata_schema, option=orjson.OPT_INDENT_2),
            "json",
            background_color="default",
        )
        table.add_row("metadata_schema", schema)

    return table

ValueMetadata (KiaraModel) pydantic-model

Source code in kiara/models/values/value_metadata/__init__.py

class ValueMetadata(KiaraModel):
    @classmethod
    @abc.abstractmethod
    def retrieve_supported_data_types(cls) -> Iterable[str]:
        pass

    @classmethod
    @abc.abstractmethod
    def create_value_metadata(
        cls, value: "Value"
    ) -> Union["ValueMetadata", Dict[str, Any]]:
        pass

    # @property
    # def metadata_key(self) -> str:
    #     return self._metadata_key  # type: ignore  # this is added by the kiara class loading functionality

    def _retrieve_id(self) -> str:
        return self._metadata_key  # type: ignore

    def _retrieve_data_to_hash(self) -> Any:
        return {"metadata": self.dict(), "schema": self.schema_json()}

create_value_metadata(value) classmethod

Source code in kiara/models/values/value_metadata/__init__.py

@classmethod
@abc.abstractmethod
def create_value_metadata(
    cls, value: "Value"
) -> Union["ValueMetadata", Dict[str, Any]]:
    pass

retrieve_supported_data_types() classmethod

Source code in kiara/models/values/value_metadata/__init__.py

@classmethod
@abc.abstractmethod
def retrieve_supported_data_types(cls) -> Iterable[str]:
    pass

Modules

included_metadata_types special

Classes

FileBundleMetadata (ValueMetadata) pydantic-model

File bundle stats.

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

class FileBundleMetadata(ValueMetadata):
    """File bundle stats."""

    _metadata_key = "file_bundle"
    _kiara_model_id = "metadata.file_bundle"

    @classmethod
    def retrieve_supported_data_types(cls) -> Iterable[str]:
        return ["file_bundle"]

    @classmethod
    def create_value_metadata(cls, value: "Value") -> "FileBundleMetadata":

        return FileBundleMetadata.construct(file_bundle=value.data)

    file_bundle: FileBundle = Field(description="The file-specific metadata.")

Attributes

file_bundle: FileBundle pydantic-field required

The file-specific metadata.

create_value_metadata(value) classmethod

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

@classmethod
def create_value_metadata(cls, value: "Value") -> "FileBundleMetadata":

    return FileBundleMetadata.construct(file_bundle=value.data)

retrieve_supported_data_types() classmethod

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

@classmethod
def retrieve_supported_data_types(cls) -> Iterable[str]:
    return ["file_bundle"]

FileMetadata (ValueMetadata) pydantic-model

File stats.

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

class FileMetadata(ValueMetadata):
    """File stats."""

    _metadata_key = "file"
    _kiara_model_id = "metadata.file"

    @classmethod
    def retrieve_supported_data_types(cls) -> Iterable[str]:
        return ["file"]

    @classmethod
    def create_value_metadata(cls, value: "Value") -> "FileMetadata":

        return FileMetadata.construct(file=value.data)

    file: FileModel = Field(description="The file-specific metadata.")

Attributes

file: FileModel pydantic-field required

The file-specific metadata.

create_value_metadata(value) classmethod

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

@classmethod
def create_value_metadata(cls, value: "Value") -> "FileMetadata":

    return FileMetadata.construct(file=value.data)

retrieve_supported_data_types() classmethod

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

@classmethod
def retrieve_supported_data_types(cls) -> Iterable[str]:
    return ["file"]

PythonClassMetadata (ValueMetadata) pydantic-model

Python class and module information.

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

class PythonClassMetadata(ValueMetadata):
    """Python class and module information."""

    _metadata_key = "python_class"
    _kiara_model_id = "metadata.python_class"

    @classmethod
    def retrieve_supported_data_types(cls) -> Iterable[str]:
        return ["any"]

    @classmethod
    def create_value_metadata(cls, value: "Value") -> "PythonClassMetadata":

        return PythonClassMetadata.construct(
            python_class=PythonClass.from_class(value.data.__class__)
        )

    # metadata_key: Literal["python_class"]
    python_class: PythonClass = Field(
        description="Details about the Python class that backs this value."
    )

Attributes

python_class: PythonClass pydantic-field required

Details about the Python class that backs this value.

create_value_metadata(value) classmethod

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

@classmethod
def create_value_metadata(cls, value: "Value") -> "PythonClassMetadata":

    return PythonClassMetadata.construct(
        python_class=PythonClass.from_class(value.data.__class__)
    )

retrieve_supported_data_types() classmethod

Source code in kiara/models/values/value_metadata/included_metadata_types/__init__.py

@classmethod
def retrieve_supported_data_types(cls) -> Iterable[str]:
    return ["any"]

value_schema

Classes

ValueSchema (KiaraModel) pydantic-model

The schema of a value.

The schema contains the [ValueTypeOrm][kiara.data.values.ValueTypeOrm] of a value, as well as an optional default that will be used if no user input was given (yet) for a value.

For more complex container data_types like array, tables, unions etc, data_types can also be configured with values from the type_config field.

Source code in kiara/models/values/value_schema.py

class ValueSchema(KiaraModel):
    """The schema of a value.

    The schema contains the [ValueTypeOrm][kiara.data.values.ValueTypeOrm] of a value, as well as an optional default that
    will be used if no user input was given (yet) for a value.

    For more complex container data_types like array, tables, unions etc, data_types can also be configured with values from the ``type_config`` field.
    """

    _kiara_model_id = "instance.value_schema"

    class Config:
        use_enum_values = True
        # extra = Extra.forbid

    type: str = Field(description="The type of the value.")
    type_config: typing.Dict[str, typing.Any] = Field(
        description="Configuration for the type, in case it's complex.",
        default_factory=dict,
    )
    default: typing.Any = Field(
        description="A default value.", default=SpecialValue.NOT_SET
    )

    optional: bool = Field(
        description="Whether this value is required (True), or whether 'None' value is allowed (False).",
        default=False,
    )
    is_constant: bool = Field(
        description="Whether the value is a constant.", default=False
    )

    doc: DocumentationMetadataModel = Field(
        default="-- n/a --",
        description="A description for the value of this input field.",
    )

    @validator("doc", pre=True)
    def validate_doc(cls, value):
        return DocumentationMetadataModel.create(value)

    def _retrieve_data_to_hash(self) -> typing.Any:

        return {"type": self.type, "type_config": self.type_config}

    def is_required(self):

        if self.optional:
            return False
        else:
            if self.default in [None, SpecialValue.NOT_SET, SpecialValue.NO_VALUE]:
                return True
            else:
                return False

    # def validate_types(self, kiara: "Kiara"):
    #
    #     if self.type not in kiara.value_type_names:
    #         raise ValueError(
    #             f"Invalid value type '{self.type}', available data_types: {kiara.value_type_names}"
    #         )

    def __eq__(self, other):

        if not isinstance(other, ValueSchema):
            return False

        return (self.type, self.default) == (other.type, other.default)

    def __hash__(self):

        return hash((self.type, self.default))

    def __repr__(self):

        return f"ValueSchema(type={self.type}, default={self.default}, optional={self.optional})"

    def __str__(self):

        return self.__repr__()

Attributes

default: Any pydantic-field

A default value.

doc: DocumentationMetadataModel pydantic-field

A description for the value of this input field.

is_constant: bool pydantic-field

Whether the value is a constant.

optional: bool pydantic-field

Whether this value is required (True), or whether 'None' value is allowed (False).

type: str pydantic-field required

The type of the value.

type_config: Dict[str, Any] pydantic-field

Configuration for the type, in case it's complex.

Config

Source code in kiara/models/values/value_schema.py

class Config:
    use_enum_values = True
    # extra = Extra.forbid

is_required(self)

Source code in kiara/models/values/value_schema.py

def is_required(self):

    if self.optional:
        return False
    else:
        if self.default in [None, SpecialValue.NOT_SET, SpecialValue.NO_VALUE]:
            return True
        else:
            return False

validate_doc(value) classmethod

Source code in kiara/models/values/value_schema.py

@validator("doc", pre=True)
def validate_doc(cls, value):
    return DocumentationMetadataModel.create(value)

modules special

KIARA_CONFIG

ValueSetSchema

yaml

Classes

InputOutputObject (ABC)

Abstract base class for classes that define inputs and outputs schemas.

Both the 'create_inputs_schemaandcreawte_outputs_schema` methods implemented by child classes return a description of the input schema of this module.

If returning a dictionary of dictionaries, the format of the return value is as follows (items with '*' are optional):

{ "[input_field_name]: { "type": "[type]", "doc*": "[a description of this input]", "optional*': [boolean whether this input is optional or required (defaults to 'False')] "[other_input_field_name]: { "type: ... ... }

Source code in kiara/modules/__init__.py

class InputOutputObject(abc.ABC):
    """Abstract base class for classes that define inputs and outputs schemas.

    Both the 'create_inputs_schema` and `creawte_outputs_schema` methods implemented by child classes return a description of the input schema of this module.

    If returning a dictionary of dictionaries, the format of the return value is as follows (items with '*' are optional):

    ```
        {
          "[input_field_name]: {
              "type": "[type]",
              "doc*": "[a description of this input]",
              "optional*': [boolean whether this input is optional or required (defaults to 'False')]
          "[other_input_field_name]: {
              "type: ...
              ...
          }
              ```
    """

    def __init__(
        self,
        alias: str,
        config: KiaraModuleConfig = None,
        allow_empty_inputs_schema: bool = False,
        allow_empty_outputs_schema: bool = False,
    ):

        self._alias: str = alias
        self._inputs_schema: Mapping[str, ValueSchema] = None  # type: ignore
        self._outputs_schema: Mapping[str, ValueSchema] = None  # type: ignore
        self._constants: Mapping[str, ValueSchema] = None  # type: ignore

        if config is None:
            config = KiaraModuleConfig()
        self._config: KiaraModuleConfig = config

        self._allow_empty_inputs: bool = allow_empty_inputs_schema
        self._allow_empty_outputs: bool = allow_empty_outputs_schema

    @property
    def alias(self) -> str:
        return self._alias

    def input_required(self, input_name: str):

        if input_name not in self._inputs_schema.keys():
            raise Exception(
                f"No input '{input_name}', available inputs: {', '.join(self._inputs_schema)}"
            )

        if not self._inputs_schema[input_name].is_required():
            return False

        if input_name in self.constants.keys():
            return False
        else:
            return True

    @abstractmethod
    def create_inputs_schema(
        self,
    ) -> ValueSetSchema:
        """Return the schema for this types' inputs."""

    @abstractmethod
    def create_outputs_schema(
        self,
    ) -> ValueSetSchema:
        """Return the schema for this types' outputs."""

    @property
    def inputs_schema(self) -> Mapping[str, ValueSchema]:
        """The input schema for this module."""

        if self._inputs_schema is None:
            self._create_inputs_schema()

        return self._inputs_schema  # type: ignore

    @property
    def constants(self) -> Mapping[str, ValueSchema]:

        if self._constants is None:
            self._create_inputs_schema()
        return self._constants  # type: ignore

    def _create_inputs_schema(self) -> None:

        try:
            _input_schemas_data = self.create_inputs_schema()

            if _input_schemas_data is None:
                raise Exception(
                    f"Invalid inputs implementation for '{self.alias}': no inputs schema"
                )

            if not _input_schemas_data and not self._allow_empty_inputs:
                raise Exception(
                    f"Invalid inputs implementation for '{self.alias}': empty inputs schema"
                )
            try:
                _input_schemas = create_schema_dict(schema_config=_input_schemas_data)
            except Exception as e:
                raise Exception(f"Can't create input schemas for '{self.alias}': {e}")

            defaults = self._config.defaults
            constants = self._config.constants

            for k, v in defaults.items():
                if k not in _input_schemas.keys():
                    raise Exception(
                        f"Can't create inputs for '{self.alias}', invalid default field name '{k}'. Available field names: '{', '.join(_input_schemas.keys())}'"  # type: ignore
                    )

            for k, v in constants.items():
                if k not in _input_schemas.keys():
                    raise Exception(
                        f"Can't create inputs for '{self.alias}', invalid constant field name '{k}'. Available field names: '{', '.join(_input_schemas.keys())}'"  # type: ignore
                    )

            self._inputs_schema, self._constants = overlay_constants_and_defaults(
                _input_schemas, defaults=defaults, constants=constants
            )

        except Exception as e:
            raise Exception(f"Can't create input schemas for instance '{self.alias}': {e}")  # type: ignore

    @property
    def outputs_schema(self) -> Mapping[str, ValueSchema]:
        """The output schema for this module."""

        if self._outputs_schema is not None:
            return self._outputs_schema

        try:
            _output_schema = self.create_outputs_schema()

            if _output_schema is None:
                raise Exception(
                    f"Invalid outputs implementation for '{self.alias}': no outputs schema"
                )

            if not _output_schema and not self._allow_empty_outputs:
                raise Exception(
                    f"Invalid outputs implementation for '{self.alias}': empty outputs schema"
                )

            try:
                self._outputs_schema = create_schema_dict(schema_config=_output_schema)
            except Exception as e:
                raise Exception(
                    f"Can't create output schemas for module {self.alias}: {e}"
                )

            return self._outputs_schema
        except Exception as e:
            if is_debug():
                import traceback

                traceback.print_exc()
            raise Exception(f"Can't create output schemas for instance of module '{self.alias}': {e}")  # type: ignore

    @property
    def input_names(self) -> Iterable[str]:
        """A list of input field names for this module."""
        return self.inputs_schema.keys()

    @property
    def output_names(self) -> Iterable[str]:
        """A list of output field names for this module."""
        return self.outputs_schema.keys()

    def augment_module_inputs(self, inputs: Mapping[str, Any]) -> Dict[str, Any]:
        return augment_values(
            values=inputs, schemas=self.inputs_schema, constants=self.constants
        )

    # def augment_outputs(self, outputs: Mapping[str, Any]) -> Dict[str, Any]:
    #     return augment_values(values=outputs, schemas=self.outputs_schema)

Attributes

alias: str property readonly

constants: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

input_names: Iterable[str] property readonly

A list of input field names for this module.

inputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

The input schema for this module.

output_names: Iterable[str] property readonly

A list of output field names for this module.

outputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] property readonly

The output schema for this module.

Methods

augment_module_inputs(self, inputs)

Source code in kiara/modules/__init__.py

def augment_module_inputs(self, inputs: Mapping[str, Any]) -> Dict[str, Any]:
    return augment_values(
        values=inputs, schemas=self.inputs_schema, constants=self.constants
    )

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/__init__.py

@abstractmethod
def create_inputs_schema(
    self,
) -> ValueSetSchema:
    """Return the schema for this types' inputs."""

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/__init__.py

@abstractmethod
def create_outputs_schema(
    self,
) -> ValueSetSchema:
    """Return the schema for this types' outputs."""

input_required(self, input_name)

Source code in kiara/modules/__init__.py

def input_required(self, input_name: str):

    if input_name not in self._inputs_schema.keys():
        raise Exception(
            f"No input '{input_name}', available inputs: {', '.join(self._inputs_schema)}"
        )

    if not self._inputs_schema[input_name].is_required():
        return False

    if input_name in self.constants.keys():
        return False
    else:
        return True

KiaraModule (InputOutputObject, Generic)

The base class that every custom module in Kiara needs to inherit from.

The core of every KiaraModule is a process method, which should be a 'pure', idempotent function that creates one or several output values from the given input(s), and its purpose is to transfor a set of inputs into a set of outputs.

Every module can be configured. The module configuration schema can differ, but every one such configuration needs to subclass the [KiaraModuleConfig][kiara.module_config.KiaraModuleConfig] class and set as the value to the _config_cls attribute of the module class. This is useful, because it allows for some modules to serve a much larger variety of use-cases than non-configurable modules would be, which would mean more code duplication because of very simlilar, but slightly different module data_types.

Each module class (type) has a unique -- within a kiara context -- module type id which can be accessed via the _module_type_name class attribute.

Examples:

A simple example would be an 'addition' module, with a and b configured as inputs, and z as the output field name.

An implementing class would look something like this:

TODO

Parameters:

Name	Type	Description	Default
module_config	Union[NoneType, ~KIARA_CONFIG, Mapping[str, Any]]	the configuation for this module	None

Source code in kiara/modules/__init__.py

class KiaraModule(InputOutputObject, Generic[KIARA_CONFIG]):
    """The base class that every custom module in *Kiara* needs to inherit from.

    The core of every ``KiaraModule`` is a ``process`` method, which should be a 'pure',
     idempotent function that creates one or several output values from the given input(s), and its purpose is to transfor
     a set of inputs into a set of outputs.

     Every module can be configured. The module configuration schema can differ, but every one such configuration needs to
     subclass the [KiaraModuleConfig][kiara.module_config.KiaraModuleConfig] class and set as the value to the
     ``_config_cls`` attribute of the module class. This is useful, because it allows for some modules to serve a much
     larger variety of use-cases than non-configurable modules would be, which would mean more code duplication because
     of very simlilar, but slightly different module data_types.

     Each module class (type) has a unique -- within a *kiara* context -- module type id which can be accessed via the
     ``_module_type_name`` class attribute.

    Examples:

        A simple example would be an 'addition' module, with ``a`` and ``b`` configured as inputs, and ``z`` as the output field name.

        An implementing class would look something like this:

        TODO

    Arguments:
        module_config: the configuation for this module
    """

    # TODO: not quite sure about this generic type here, mypy doesn't seem to like it
    _config_cls: Type[KIARA_CONFIG] = KiaraModuleConfig  # type: ignore

    @classmethod
    def is_pipeline(cls) -> bool:
        """Check whether this module type is a pipeline, or not."""
        return False

    @classmethod
    def _calculate_module_cid(
        cls, module_type_config: Union[Mapping[str, Any], KIARA_CONFIG]
    ) -> CID:

        if isinstance(module_type_config, Mapping):
            module_type_config = cls._config_cls(**module_type_config)

        obj = {
            "module_type": cls._module_type_name,  # type: ignore
            "module_type_config": module_type_config.dict(),  # type: ignore
        }
        _, cid = compute_cid(data=obj)
        return cid

    def __init__(
        self,
        module_config: Union[None, KIARA_CONFIG, Mapping[str, Any]] = None,
    ):
        self._id: uuid.UUID = uuid.uuid4()

        if isinstance(module_config, KiaraModuleConfig):
            self._config: KIARA_CONFIG = module_config  # type: ignore
        elif module_config is None:
            self._config = self.__class__._config_cls()
        elif isinstance(module_config, Mapping):
            try:
                self._config = self.__class__._config_cls(**module_config)
            except ValidationError as ve:
                raise KiaraModuleConfigException(
                    f"Error creating module '{id}'. {ve}",
                    self.__class__,
                    module_config,
                    ve,
                )
        else:
            raise TypeError(f"Invalid type for module config: {type(module_config)}")

        self._module_cid: Optional[CID] = None
        self._characteristics: Optional[ModuleCharacteristics] = None

        super().__init__(alias=self.__class__._module_type_name, config=self._config)  # type: ignore

        self._operation: Optional[Operation] = None
        # self._merged_input_schemas: typing.Mapping[str, ValueSchema] = None  # type: ignore

    @property
    def module_id(self) -> uuid.UUID:
        """The id of this module."""
        return self._id

    @property
    def module_type_name(self) -> str:
        if not self._module_type_name:  # type: ignore
            raise Exception(
                f"Module class '{self.__class__.__name__}' does not have a '_module_type_name' attribute. This is a bug."
            )
        return self._module_type_name  # type: ignore

    @property
    def config(self) -> KIARA_CONFIG:
        """Retrieve the configuration object for this module.

        Returns:
            the module-class-specific config object
        """
        return self._config

    @property
    def module_instance_cid(self) -> CID:

        if self._module_cid is None:
            self._module_cid = self.__class__._calculate_module_cid(self._config)
        return self._module_cid

    @property
    def characteristics(self) -> ModuleCharacteristics:
        if self._characteristics is not None:
            return self._characteristics

        self._characteristics = self._retrieve_module_characteristics()
        return self._characteristics

    def _retrieve_module_characteristics(self) -> ModuleCharacteristics:
        return ModuleCharacteristics()

    def get_config_value(self, key: str) -> Any:
        """Retrieve the value for a specific configuration option.

        Arguments:
            key: the config key

        Returns:
            the value for the provided key
        """

        try:
            return self.config.get(key)
        except Exception:
            raise Exception(
                f"Error accessing config value '{key}' in module {self.__class__._module_type_name}."  # type: ignore
            )

    def process_step(
        self, inputs: "ValueMap", outputs: "ValueMap", job_log: JobLog
    ) -> None:
        """Kick off processing for a specific set of input/outputs.

        This method calls the implemented [process][kiara.module.KiaraModule.process] method of the inheriting class,
        as well as wrapping input/output-data related functionality.

        Arguments:
            inputs: the input value set
            outputs: the output value set
        """

        signature = inspect.signature(self.process)  # type: ignore

        if "job_log" not in signature.parameters.keys():

            try:
                self.process(inputs=inputs, outputs=outputs)  # type: ignore
            except Exception as e:
                if is_debug():
                    try:
                        import traceback

                        traceback.print_exc()
                    except Exception:
                        pass
                raise e

        else:

            try:
                self.process(inputs=inputs, outputs=outputs, job_log=job_log)  # type: ignore
            except Exception as e:
                if is_debug():
                    try:
                        import traceback

                        traceback.print_exc()
                    except Exception:
                        pass
                raise e

    def __eq__(self, other):
        if self.__class__ != other.__class__:
            return False
        return self.module_instance_cid == other.module_instance_cid

    def __hash__(self):
        return int.from_bytes(self.module_instance_cid.digest, "big")

    def __repr__(self):
        return f"{self.__class__.__name__}(id={self.module_id} module_type={self.module_type_name} input_names={list(self.input_names)} output_names={list(self.output_names)})"

    def create_renderable(self, **config) -> RenderableType:

        if self._operation is not None:
            return self._operation

        from kiara.models.module.operation import Operation

        self._operation = Operation.create_from_module(self)
        return self._operation

Attributes

characteristics: ModuleCharacteristics property readonly

config: ~KIARA_CONFIG property readonly

Retrieve the configuration object for this module.

Returns:

Type	Description
~KIARA_CONFIG	the module-class-specific config object

module_id: UUID property readonly

The id of this module.

module_instance_cid: CID property readonly

module_type_name: str property readonly

Classes

_config_cls (KiaraModel) private pydantic-model

Base class that describes the configuration a [KiaraModule][kiara.module.KiaraModule] class accepts.

This is stored in the _config_cls class attribute in each KiaraModule class.

There are two config options every KiaraModule supports:

• constants, and
• defaults

Constants are pre-set inputs, and users can't change them and an error is thrown if they try. Defaults are default values that override the schema defaults, and those can be overwritten by users. If both a constant and a default value is set for an input field, an error is thrown.

Source code in kiara/modules/__init__.py

class KiaraModuleConfig(KiaraModel):
    """Base class that describes the configuration a [``KiaraModule``][kiara.module.KiaraModule] class accepts.

    This is stored in the ``_config_cls`` class attribute in each ``KiaraModule`` class.

    There are two config options every ``KiaraModule`` supports:

     - ``constants``, and
     - ``defaults``

     Constants are pre-set inputs, and users can't change them and an error is thrown if they try. Defaults are default
     values that override the schema defaults, and those can be overwritten by users. If both a constant and a default
     value is set for an input field, an error is thrown.
    """

    _kiara_model_id = "instance.module_config"

    @classmethod
    def requires_config(cls, config: Optional[Mapping[str, Any]] = None) -> bool:
        """Return whether this class can be used as-is, or requires configuration before an instance can be created."""

        for field_name, field in cls.__fields__.items():
            if field.required and field.default is None:
                if config:
                    if config.get(field_name, None) is None:
                        return True
                else:
                    return True
        return False

    _config_hash: str = PrivateAttr(default=None)
    constants: Dict[str, Any] = Field(
        default_factory=dict, description="Value constants for this module."
    )
    defaults: Dict[str, Any] = Field(
        default_factory=dict, description="Value defaults for this module."
    )

    class Config:
        extra = Extra.forbid
        validate_assignment = True

    def get(self, key: str) -> Any:
        """Get the value for the specified configuation key."""

        if key not in self.__fields__:
            raise Exception(
                f"No config value '{key}' in module config class '{self.__class__.__name__}'."
            )

        return getattr(self, key)

    def create_renderable(self, **config: Any) -> RenderableType:

        my_table = Table(box=box.MINIMAL, show_header=False)
        my_table.add_column("Field name", style="i")
        my_table.add_column("Value")
        for field in self.__fields__:
            attr = getattr(self, field)
            if isinstance(attr, str):
                attr_str = attr
            elif hasattr(attr, "create_renderable"):
                attr_str = attr.create_renderable()
            elif isinstance(attr, BaseModel):
                attr_str = attr.json(option=orjson.orjson.OPT_INDENT_2)
            else:
                attr_str = str(attr)
            my_table.add_row(field, attr_str)

        return my_table

Attributes

constants: Dict[str, Any] pydantic-field

Value constants for this module.

defaults: Dict[str, Any] pydantic-field

Value defaults for this module.

Config

Source code in kiara/modules/__init__.py

class Config:
    extra = Extra.forbid
    validate_assignment = True

extra

validate_assignment

Methods

create_renderable(self, **config)

Source code in kiara/modules/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:

    my_table = Table(box=box.MINIMAL, show_header=False)
    my_table.add_column("Field name", style="i")
    my_table.add_column("Value")
    for field in self.__fields__:
        attr = getattr(self, field)
        if isinstance(attr, str):
            attr_str = attr
        elif hasattr(attr, "create_renderable"):
            attr_str = attr.create_renderable()
        elif isinstance(attr, BaseModel):
            attr_str = attr.json(option=orjson.orjson.OPT_INDENT_2)
        else:
            attr_str = str(attr)
        my_table.add_row(field, attr_str)

    return my_table

get(self, key)

Get the value for the specified configuation key.

Source code in kiara/modules/__init__.py

def get(self, key: str) -> Any:
    """Get the value for the specified configuation key."""

    if key not in self.__fields__:
        raise Exception(
            f"No config value '{key}' in module config class '{self.__class__.__name__}'."
        )

    return getattr(self, key)

requires_config(config=None) classmethod

Return whether this class can be used as-is, or requires configuration before an instance can be created.

Source code in kiara/modules/__init__.py

@classmethod
def requires_config(cls, config: Optional[Mapping[str, Any]] = None) -> bool:
    """Return whether this class can be used as-is, or requires configuration before an instance can be created."""

    for field_name, field in cls.__fields__.items():
        if field.required and field.default is None:
            if config:
                if config.get(field_name, None) is None:
                    return True
            else:
                return True
    return False

Methods

create_renderable(self, **config)

Source code in kiara/modules/__init__.py

def create_renderable(self, **config) -> RenderableType:

    if self._operation is not None:
        return self._operation

    from kiara.models.module.operation import Operation

    self._operation = Operation.create_from_module(self)
    return self._operation

get_config_value(self, key)

Retrieve the value for a specific configuration option.

Parameters:

Name	Type	Description	Default
key	str	the config key	required

Returns:

Type	Description
Any	the value for the provided key

Source code in kiara/modules/__init__.py

def get_config_value(self, key: str) -> Any:
    """Retrieve the value for a specific configuration option.

    Arguments:
        key: the config key

    Returns:
        the value for the provided key
    """

    try:
        return self.config.get(key)
    except Exception:
        raise Exception(
            f"Error accessing config value '{key}' in module {self.__class__._module_type_name}."  # type: ignore
        )

is_pipeline() classmethod

Check whether this module type is a pipeline, or not.

Source code in kiara/modules/__init__.py

@classmethod
def is_pipeline(cls) -> bool:
    """Check whether this module type is a pipeline, or not."""
    return False

process_step(self, inputs, outputs, job_log)

Kick off processing for a specific set of input/outputs.

This method calls the implemented [process][kiara.module.KiaraModule.process] method of the inheriting class, as well as wrapping input/output-data related functionality.

Parameters:

Name	Type	Description	Default
inputs	ValueMap	the input value set	required
outputs	ValueMap	the output value set	required

Source code in kiara/modules/__init__.py

def process_step(
    self, inputs: "ValueMap", outputs: "ValueMap", job_log: JobLog
) -> None:
    """Kick off processing for a specific set of input/outputs.

    This method calls the implemented [process][kiara.module.KiaraModule.process] method of the inheriting class,
    as well as wrapping input/output-data related functionality.

    Arguments:
        inputs: the input value set
        outputs: the output value set
    """

    signature = inspect.signature(self.process)  # type: ignore

    if "job_log" not in signature.parameters.keys():

        try:
            self.process(inputs=inputs, outputs=outputs)  # type: ignore
        except Exception as e:
            if is_debug():
                try:
                    import traceback

                    traceback.print_exc()
                except Exception:
                    pass
            raise e

    else:

        try:
            self.process(inputs=inputs, outputs=outputs, job_log=job_log)  # type: ignore
        except Exception as e:
            if is_debug():
                try:
                    import traceback

                    traceback.print_exc()
                except Exception:
                    pass
            raise e

ModuleCharacteristics (BaseModel) pydantic-model

Source code in kiara/modules/__init__.py

class ModuleCharacteristics(BaseModel):

    is_idempotent: bool = Field(
        description="Whether this module is idempotent (aka always produces the same output with the same inputs.",
        default=False,
    )
    is_internal: bool = Field(
        description="Hint for frontends whether this module is used predominantly internally, and users won't need to know of its existence.",
        default=False,
    )

Attributes

is_idempotent: bool pydantic-field

Whether this module is idempotent (aka always produces the same output with the same inputs.

is_internal: bool pydantic-field

Hint for frontends whether this module is used predominantly internally, and users won't need to know of its existence.

Modules

included_core_modules special

Modules

create_from

Classes

CreateFromModule (KiaraModule)

Source code in kiara/modules/included_core_modules/create_from.py

class CreateFromModule(KiaraModule):

    _module_type_name: str = None  # type: ignore
    _config_cls = CreateFromModuleConfig

    @classmethod
    def retrieve_supported_create_combinations(cls) -> Iterable[Mapping[str, str]]:

        result = []
        for attr in dir(cls):
            if (
                len(attr) <= 16
                or not attr.startswith("create__")
                or "__from__" not in attr
            ):
                continue

            tokens = attr.split("__")
            if len(tokens) != 4:
                continue

            source_type = tokens[3]
            target_type = tokens[1]

            data = {
                "source_type": source_type,
                "target_type": target_type,
                "func": attr,
            }
            result.append(data)
        return result

    def create_optional_inputs(
        self, source_type: str, target_type
    ) -> Optional[Mapping[str, Mapping[str, Any]]]:
        return None

    def create_inputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        source_type = self.get_config_value("source_type")
        assert source_type not in ["target", "base_name"]

        target_type = self.get_config_value("target_type")
        optional = self.create_optional_inputs(
            source_type=source_type, target_type=target_type
        )

        schema = {
            source_type: {"type": source_type, "doc": "The type of the source value."},
        }
        if optional:
            for field, field_schema in optional.items():
                field_schema = dict(field_schema)
                if field in schema.keys():
                    raise Exception(
                        f"Can't create inputs schema for '{self.module_type_name}': duplicate field '{field}'."
                    )
                if field == source_type:
                    raise Exception(
                        f"Can't create inputs schema for '{self.module_type_name}': invalid field name '{field}'."
                    )

                optional = field_schema.get("optional", True)
                if not optional:
                    raise Exception(
                        f"Can't create inputs schema for '{self.module_type_name}': non-optional field '{field}' specified."
                    )
                field_schema["optional"] = True
                schema[field] = field_schema
        return schema

    def create_outputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        return {
            self.get_config_value("target_type"): {
                "type": self.get_config_value("target_type"),
                "doc": "The result value.",
            }
        }

    def process(self, inputs: ValueMap, outputs: ValueMap) -> None:

        source_type = self.get_config_value("source_type")
        target_type = self.get_config_value("target_type")

        func_name = f"create__{target_type}__from__{source_type}"
        func = getattr(self, func_name)

        source_value = inputs.get_value_obj(source_type)

        signature = inspect.signature(func)
        if "optional" in signature.parameters:
            optional: Dict[str, Value] = {}
            op_schemas = {}
            for field, schema in self.inputs_schema.items():
                if field == source_type:
                    continue
                optional[field] = inputs.get_value_obj(field)
                op_schemas[field] = schema
            result = func(
                source_value=source_value,
                optional=ValueMapReadOnly(
                    value_items=optional, values_schema=op_schemas
                ),
            )
        else:
            result = func(source_value=source_value)
        outputs.set_value(target_type, result)

Classes

_config_cls (KiaraModuleConfig) private pydantic-model

Source code in kiara/modules/included_core_modules/create_from.py

class CreateFromModuleConfig(KiaraModuleConfig):

    source_type: str = Field(description="The value type of the source value.")
    target_type: str = Field(description="The value type of the target.")

Attributes

source_type: str pydantic-field required

The value type of the source value.

target_type: str pydantic-field required

The value type of the target.

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/create_from.py

def create_inputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    source_type = self.get_config_value("source_type")
    assert source_type not in ["target", "base_name"]

    target_type = self.get_config_value("target_type")
    optional = self.create_optional_inputs(
        source_type=source_type, target_type=target_type
    )

    schema = {
        source_type: {"type": source_type, "doc": "The type of the source value."},
    }
    if optional:
        for field, field_schema in optional.items():
            field_schema = dict(field_schema)
            if field in schema.keys():
                raise Exception(
                    f"Can't create inputs schema for '{self.module_type_name}': duplicate field '{field}'."
                )
            if field == source_type:
                raise Exception(
                    f"Can't create inputs schema for '{self.module_type_name}': invalid field name '{field}'."
                )

            optional = field_schema.get("optional", True)
            if not optional:
                raise Exception(
                    f"Can't create inputs schema for '{self.module_type_name}': non-optional field '{field}' specified."
                )
            field_schema["optional"] = True
            schema[field] = field_schema
    return schema

create_optional_inputs(self, source_type, target_type)

Source code in kiara/modules/included_core_modules/create_from.py

def create_optional_inputs(
    self, source_type: str, target_type
) -> Optional[Mapping[str, Mapping[str, Any]]]:
    return None

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/create_from.py

def create_outputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    return {
        self.get_config_value("target_type"): {
            "type": self.get_config_value("target_type"),
            "doc": "The result value.",
        }
    }

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/create_from.py

def process(self, inputs: ValueMap, outputs: ValueMap) -> None:

    source_type = self.get_config_value("source_type")
    target_type = self.get_config_value("target_type")

    func_name = f"create__{target_type}__from__{source_type}"
    func = getattr(self, func_name)

    source_value = inputs.get_value_obj(source_type)

    signature = inspect.signature(func)
    if "optional" in signature.parameters:
        optional: Dict[str, Value] = {}
        op_schemas = {}
        for field, schema in self.inputs_schema.items():
            if field == source_type:
                continue
            optional[field] = inputs.get_value_obj(field)
            op_schemas[field] = schema
        result = func(
            source_value=source_value,
            optional=ValueMapReadOnly(
                value_items=optional, values_schema=op_schemas
            ),
        )
    else:
        result = func(source_value=source_value)
    outputs.set_value(target_type, result)

retrieve_supported_create_combinations() classmethod

Source code in kiara/modules/included_core_modules/create_from.py

@classmethod
def retrieve_supported_create_combinations(cls) -> Iterable[Mapping[str, str]]:

    result = []
    for attr in dir(cls):
        if (
            len(attr) <= 16
            or not attr.startswith("create__")
            or "__from__" not in attr
        ):
            continue

        tokens = attr.split("__")
        if len(tokens) != 4:
            continue

        source_type = tokens[3]
        target_type = tokens[1]

        data = {
            "source_type": source_type,
            "target_type": target_type,
            "func": attr,
        }
        result.append(data)
    return result

CreateFromModuleConfig (KiaraModuleConfig) pydantic-model

Source code in kiara/modules/included_core_modules/create_from.py

class CreateFromModuleConfig(KiaraModuleConfig):

    source_type: str = Field(description="The value type of the source value.")
    target_type: str = Field(description="The value type of the target.")

Attributes

source_type: str pydantic-field required

The value type of the source value.

target_type: str pydantic-field required

The value type of the target.

filesystem

Classes

DeserializeFileBundleModule (DeserializeValueModule)

Deserialize data to a 'file' value instance.

Source code in kiara/modules/included_core_modules/filesystem.py

class DeserializeFileBundleModule(DeserializeValueModule):
    """Deserialize data to a 'file' value instance."""

    _module_type_name = "deserialize.file_bundle"

    @classmethod
    def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
        return {"python_object": FileBundle}

    @classmethod
    def retrieve_serialized_value_type(cls) -> str:
        return "file_bundle"

    @classmethod
    def retrieve_supported_serialization_profile(cls) -> str:
        return "copy"

    def to__python_object(self, data: SerializedData, **config: Any):

        keys = list(data.get_keys())
        keys.remove("__file_metadata__")

        file_metadata_chunks = data.get_serialized_data("__file_metadata__")
        assert file_metadata_chunks.get_number_of_chunks() == 1
        file_metadata_json = list(file_metadata_chunks.get_chunks(as_files=False))
        assert len(file_metadata_json) == 1
        metadata = orjson.loads(file_metadata_json[0])
        file_metadata = metadata["included_files"]
        bundle_name = metadata["bundle_name"]
        bundle_import_time = metadata["import_time"]
        sum_size = metadata["size"]
        number_of_files = metadata["number_of_files"]

        included_files = {}
        for rel_path in keys:

            chunks = data.get_serialized_data(rel_path)
            assert chunks.get_number_of_chunks() == 1

            files = list(chunks.get_chunks(as_files=True, symlink_ok=True))
            assert len(files) == 1

            file: str = files[0]  # type: ignore
            file_name = file_metadata[rel_path]["file_name"]
            import_time = file_metadata[rel_path]["import_time"]
            fm = FileModel.load_file(
                source=file, file_name=file_name, import_time=import_time
            )
            included_files[rel_path] = fm

        fb = FileBundle(
            included_files=included_files,
            bundle_name=bundle_name,
            import_time=bundle_import_time,
            number_of_files=number_of_files,
            size=sum_size,
        )
        return fb

retrieve_serialized_value_type() classmethod

Source code in kiara/modules/included_core_modules/filesystem.py

@classmethod
def retrieve_serialized_value_type(cls) -> str:
    return "file_bundle"

retrieve_supported_serialization_profile() classmethod

Source code in kiara/modules/included_core_modules/filesystem.py

@classmethod
def retrieve_supported_serialization_profile(cls) -> str:
    return "copy"

retrieve_supported_target_profiles() classmethod

Source code in kiara/modules/included_core_modules/filesystem.py

@classmethod
def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
    return {"python_object": FileBundle}

to__python_object(self, data, **config)

Source code in kiara/modules/included_core_modules/filesystem.py

def to__python_object(self, data: SerializedData, **config: Any):

    keys = list(data.get_keys())
    keys.remove("__file_metadata__")

    file_metadata_chunks = data.get_serialized_data("__file_metadata__")
    assert file_metadata_chunks.get_number_of_chunks() == 1
    file_metadata_json = list(file_metadata_chunks.get_chunks(as_files=False))
    assert len(file_metadata_json) == 1
    metadata = orjson.loads(file_metadata_json[0])
    file_metadata = metadata["included_files"]
    bundle_name = metadata["bundle_name"]
    bundle_import_time = metadata["import_time"]
    sum_size = metadata["size"]
    number_of_files = metadata["number_of_files"]

    included_files = {}
    for rel_path in keys:

        chunks = data.get_serialized_data(rel_path)
        assert chunks.get_number_of_chunks() == 1

        files = list(chunks.get_chunks(as_files=True, symlink_ok=True))
        assert len(files) == 1

        file: str = files[0]  # type: ignore
        file_name = file_metadata[rel_path]["file_name"]
        import_time = file_metadata[rel_path]["import_time"]
        fm = FileModel.load_file(
            source=file, file_name=file_name, import_time=import_time
        )
        included_files[rel_path] = fm

    fb = FileBundle(
        included_files=included_files,
        bundle_name=bundle_name,
        import_time=bundle_import_time,
        number_of_files=number_of_files,
        size=sum_size,
    )
    return fb

DeserializeFileModule (DeserializeValueModule)

Deserialize data to a 'file' value instance.

Source code in kiara/modules/included_core_modules/filesystem.py

class DeserializeFileModule(DeserializeValueModule):
    """Deserialize data to a 'file' value instance."""

    _module_type_name = "deserialize.file"

    @classmethod
    def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
        return {"python_object": FileModel}

    @classmethod
    def retrieve_serialized_value_type(cls) -> str:
        return "file"

    @classmethod
    def retrieve_supported_serialization_profile(cls) -> str:
        return "copy"

    def to__python_object(self, data: SerializedData, **config: Any):

        keys = list(data.get_keys())
        keys.remove("__file_metadata__")
        assert len(keys) == 1

        file_metadata_chunks = data.get_serialized_data("__file_metadata__")
        assert file_metadata_chunks.get_number_of_chunks() == 1
        file_metadata_json = list(file_metadata_chunks.get_chunks(as_files=False))
        assert len(file_metadata_json) == 1
        file_metadata = orjson.loads(file_metadata_json[0])

        chunks = data.get_serialized_data(keys[0])
        assert chunks.get_number_of_chunks() == 1

        files = list(chunks.get_chunks(as_files=True, symlink_ok=True))
        assert len(files) == 1

        file: str = files[0]  # type: ignore

        fm = FileModel.load_file(
            source=file,
            file_name=file_metadata["file_name"],
            import_time=file_metadata["import_time"],
        )
        return fm

retrieve_serialized_value_type() classmethod

Source code in kiara/modules/included_core_modules/filesystem.py

@classmethod
def retrieve_serialized_value_type(cls) -> str:
    return "file"

retrieve_supported_serialization_profile() classmethod

Source code in kiara/modules/included_core_modules/filesystem.py

@classmethod
def retrieve_supported_serialization_profile(cls) -> str:
    return "copy"

retrieve_supported_target_profiles() classmethod

Source code in kiara/modules/included_core_modules/filesystem.py

@classmethod
def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
    return {"python_object": FileModel}

to__python_object(self, data, **config)

Source code in kiara/modules/included_core_modules/filesystem.py

def to__python_object(self, data: SerializedData, **config: Any):

    keys = list(data.get_keys())
    keys.remove("__file_metadata__")
    assert len(keys) == 1

    file_metadata_chunks = data.get_serialized_data("__file_metadata__")
    assert file_metadata_chunks.get_number_of_chunks() == 1
    file_metadata_json = list(file_metadata_chunks.get_chunks(as_files=False))
    assert len(file_metadata_json) == 1
    file_metadata = orjson.loads(file_metadata_json[0])

    chunks = data.get_serialized_data(keys[0])
    assert chunks.get_number_of_chunks() == 1

    files = list(chunks.get_chunks(as_files=True, symlink_ok=True))
    assert len(files) == 1

    file: str = files[0]  # type: ignore

    fm = FileModel.load_file(
        source=file,
        file_name=file_metadata["file_name"],
        import_time=file_metadata["import_time"],
    )
    return fm

ImportFileBundleModule (KiaraModule)

Import a folder (file_bundle) from the local filesystem.

Source code in kiara/modules/included_core_modules/filesystem.py

class ImportFileBundleModule(KiaraModule):
    """Import a folder (file_bundle) from the local filesystem."""

    _module_type_name = "import.file_bundle"

    def create_inputs_schema(
        self,
    ) -> ValueSetSchema:

        return {
            "path": {"type": "string", "doc": "The local path of the folder to import."}
        }

    def create_outputs_schema(
        self,
    ) -> ValueSetSchema:

        return {
            "file_bundle": {"type": "file_bundle", "doc": "The imported file bundle."}
        }

    def process(self, inputs: ValueMap, outputs: ValueMap):

        path = inputs.get_value_data("path")

        file_bundle = FileBundle.import_folder(source=path)
        outputs.set_value("file_bundle", file_bundle)

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/filesystem.py

def create_inputs_schema(
    self,
) -> ValueSetSchema:

    return {
        "path": {"type": "string", "doc": "The local path of the folder to import."}
    }

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/filesystem.py

def create_outputs_schema(
    self,
) -> ValueSetSchema:

    return {
        "file_bundle": {"type": "file_bundle", "doc": "The imported file bundle."}
    }

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/filesystem.py

def process(self, inputs: ValueMap, outputs: ValueMap):

    path = inputs.get_value_data("path")

    file_bundle = FileBundle.import_folder(source=path)
    outputs.set_value("file_bundle", file_bundle)

ImportFileModule (KiaraModule)

Import a file from the local filesystem.

Source code in kiara/modules/included_core_modules/filesystem.py

class ImportFileModule(KiaraModule):
    """Import a file from the local filesystem."""

    _module_type_name = "import.file"

    def create_inputs_schema(
        self,
    ) -> ValueSetSchema:

        return {"path": {"type": "string", "doc": "The local path to the file."}}

    def create_outputs_schema(
        self,
    ) -> ValueSetSchema:

        return {"file": {"type": "file", "doc": "The loaded files."}}

    def process(self, inputs: ValueMap, outputs: ValueMap):

        path = inputs.get_value_data("path")

        file = FileModel.load_file(source=path)
        outputs.set_value("file", file)

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/filesystem.py

def create_inputs_schema(
    self,
) -> ValueSetSchema:

    return {"path": {"type": "string", "doc": "The local path to the file."}}

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/filesystem.py

def create_outputs_schema(
    self,
) -> ValueSetSchema:

    return {"file": {"type": "file", "doc": "The loaded files."}}

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/filesystem.py

def process(self, inputs: ValueMap, outputs: ValueMap):

    path = inputs.get_value_data("path")

    file = FileModel.load_file(source=path)
    outputs.set_value("file", file)

metadata

Classes

ExtractMetadataModule (KiaraModule)

Base class to use when writing a module to extract metadata from a file.

It's possible to use any arbitrary kiara module for this purpose, but sub-classing this makes it easier.

Source code in kiara/modules/included_core_modules/metadata.py

class ExtractMetadataModule(KiaraModule):
    """Base class to use when writing a module to extract metadata from a file.

    It's possible to use any arbitrary *kiara* module for this purpose, but sub-classing this makes it easier.
    """

    _config_cls = MetadataModuleConfig
    _module_type_name: str = "value.extract_metadata"

    def create_inputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        data_type_name = self.get_config_value("data_type")
        inputs = {
            "value": {
                "type": data_type_name,
                "doc": f"A value of type '{data_type_name}'",
                "optional": False,
            }
        }
        return inputs

    def create_outputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        kiara_model_id: str = self.get_config_value("kiara_model_id")

        # TODO: check it's subclassing the right class

        outputs = {
            "value_metadata": {
                "type": "internal_model",
                "type_config": {"kiara_model_id": kiara_model_id},
                "doc": "The metadata for the provided value.",
            }
        }

        return outputs

    def process(self, inputs: ValueMap, outputs: ValueMap) -> None:

        value = inputs.get_value_obj("value")

        kiara_model_id: str = self.get_config_value("kiara_model_id")

        model_registry = ModelRegistry.instance()
        metadata_model_cls: Type[ValueMetadata] = model_registry.get_model_cls(kiara_model_id=kiara_model_id, required_subclass=ValueMetadata)  # type: ignore

        metadata = metadata_model_cls.create_value_metadata(value=value)

        if not isinstance(metadata, metadata_model_cls):
            raise KiaraProcessingException(
                f"Invalid metadata model result, should be class '{metadata_model_cls.__name__}', but is: {metadata.__class__.__name__}. This is most likely a bug."
            )

        outputs.set_value("value_metadata", metadata)

Classes

_config_cls (KiaraModuleConfig) private pydantic-model

Source code in kiara/modules/included_core_modules/metadata.py

class MetadataModuleConfig(KiaraModuleConfig):

    data_type: str = Field(description="The data type this module will be used for.")
    kiara_model_id: str = Field(description="The id of the kiara (metadata) model.")

Attributes

data_type: str pydantic-field required

The data type this module will be used for.

kiara_model_id: str pydantic-field required

The id of the kiara (metadata) model.

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/metadata.py

def create_inputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    data_type_name = self.get_config_value("data_type")
    inputs = {
        "value": {
            "type": data_type_name,
            "doc": f"A value of type '{data_type_name}'",
            "optional": False,
        }
    }
    return inputs

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/metadata.py

def create_outputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    kiara_model_id: str = self.get_config_value("kiara_model_id")

    # TODO: check it's subclassing the right class

    outputs = {
        "value_metadata": {
            "type": "internal_model",
            "type_config": {"kiara_model_id": kiara_model_id},
            "doc": "The metadata for the provided value.",
        }
    }

    return outputs

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/metadata.py

def process(self, inputs: ValueMap, outputs: ValueMap) -> None:

    value = inputs.get_value_obj("value")

    kiara_model_id: str = self.get_config_value("kiara_model_id")

    model_registry = ModelRegistry.instance()
    metadata_model_cls: Type[ValueMetadata] = model_registry.get_model_cls(kiara_model_id=kiara_model_id, required_subclass=ValueMetadata)  # type: ignore

    metadata = metadata_model_cls.create_value_metadata(value=value)

    if not isinstance(metadata, metadata_model_cls):
        raise KiaraProcessingException(
            f"Invalid metadata model result, should be class '{metadata_model_cls.__name__}', but is: {metadata.__class__.__name__}. This is most likely a bug."
        )

    outputs.set_value("value_metadata", metadata)

MetadataModuleConfig (KiaraModuleConfig) pydantic-model

Source code in kiara/modules/included_core_modules/metadata.py

class MetadataModuleConfig(KiaraModuleConfig):

    data_type: str = Field(description="The data type this module will be used for.")
    kiara_model_id: str = Field(description="The id of the kiara (metadata) model.")

Attributes

data_type: str pydantic-field required

The data type this module will be used for.

kiara_model_id: str pydantic-field required

The id of the kiara (metadata) model.

pipeline

Classes

PipelineModule (KiaraModule)

Source code in kiara/modules/included_core_modules/pipeline.py

class PipelineModule(KiaraModule):

    _config_cls = PipelineConfig
    _module_type_name = "pipeline"

    def __init__(
        self,
        module_config: Union[None, KIARA_CONFIG, Mapping[str, Any]] = None,
    ):
        self._job_registry: Optional[JobRegistry] = None
        super().__init__(module_config=module_config)

    @classmethod
    def is_pipeline(cls) -> bool:
        return True

    def _set_job_registry(self, job_registry: "JobRegistry"):
        self._job_registry = job_registry

    def create_inputs_schema(
        self,
    ) -> ValueSetSchema:

        pipeline_structure: PipelineStructure = self.config.structure
        return pipeline_structure.pipeline_inputs_schema

    def create_outputs_schema(
        self,
    ) -> ValueSetSchema:
        pipeline_structure: PipelineStructure = self.config.structure
        return pipeline_structure.pipeline_outputs_schema

    def process(self, inputs: ValueMap, outputs: ValueMapWritable, job_log: JobLog):

        pipeline_structure: PipelineStructure = self.config.structure

        pipeline = Pipeline(
            structure=pipeline_structure, data_registry=outputs._data_registry
        )

        assert self._job_registry is not None

        controller = SinglePipelineBatchController(
            pipeline=pipeline, job_registry=self._job_registry
        )

        pipeline.set_pipeline_inputs(inputs=inputs)
        controller.process_pipeline()

        # TODO: resolve values first?
        outputs.set_values(**pipeline.get_current_pipeline_outputs())

Classes

_config_cls (KiaraModuleConfig) private pydantic-model

A class to hold the configuration for a [PipelineModule][kiara.pipeline.module.PipelineModule].

If you want to control the pipeline input and output names, you need to have to provide a map that uses the autogenerated field name ([step_id]__[alias] -- 2 underscores!!) as key, and the desired field name as value. The reason that schema for the autogenerated field names exist is that it's hard to ensure the uniqueness of each field; some steps can have the same input field names, but will need different input values. In some cases, some inputs of different steps need the same input. Those sorts of things. So, to make sure that we always use the right values, I chose to implement a conservative default approach, accepting that in some cases the user will be prompted for duplicate inputs for the same value.

To remedy that, the pipeline creator has the option to manually specify a mapping to rename some or all of the input/output fields.

Further, because in a lot of cases there won't be any overlapping fields, the creator can specify auto, in which case Kiara will automatically create a mapping that tries to map autogenerated field names to the shortest possible names for each case.

Examples:

Configuration for a pipeline module that functions as a nand logic gate (in Python):

and_step = PipelineStepConfig(module_type="and", step_id="and")
not_step = PipelineStepConfig(module_type="not", step_id="not", input_links={"a": ["and.y"]}
nand_p_conf = PipelineConfig(doc="Returns 'False' if both inputs are 'True'.",
                    steps=[and_step, not_step],
                    input_aliases={
                        "and__a": "a",
                        "and__b": "b"
                    },
                    output_aliases={
                        "not__y": "y"
                    }}

Or, the same thing in json:

{
  "module_type_name": "nand",
  "doc": "Returns 'False' if both inputs are 'True'.",
  "steps": [
    {
      "module_type": "and",
      "step_id": "and"
    },
    {
      "module_type": "not",
      "step_id": "not",
      "input_links": {
        "a": "and.y"
      }
    }
  ],
  "input_aliases": {
    "and__a": "a",
    "and__b": "b"
  },
  "output_aliases": {
    "not__y": "y"
  }
}

Source code in kiara/modules/included_core_modules/pipeline.py

class PipelineConfig(KiaraModuleConfig):
    """A class to hold the configuration for a [PipelineModule][kiara.pipeline.module.PipelineModule].

    If you want to control the pipeline input and output names, you need to have to provide a map that uses the
    autogenerated field name ([step_id]__[alias] -- 2 underscores!!) as key, and the desired field name
    as value. The reason that schema for the autogenerated field names exist is that it's hard to ensure
    the uniqueness of each field; some steps can have the same input field names, but will need different input
    values. In some cases, some inputs of different steps need the same input. Those sorts of things.
    So, to make sure that we always use the right values, I chose to implement a conservative default approach,
    accepting that in some cases the user will be prompted for duplicate inputs for the same value.

    To remedy that, the pipeline creator has the option to manually specify a mapping to rename some or all of
    the input/output fields.

    Further, because in a lot of cases there won't be any overlapping fields, the creator can specify ``auto``,
    in which case *Kiara* will automatically create a mapping that tries to map autogenerated field names
    to the shortest possible names for each case.

    Examples:

        Configuration for a pipeline module that functions as a ``nand`` logic gate (in Python):

        ``` python
        and_step = PipelineStepConfig(module_type="and", step_id="and")
        not_step = PipelineStepConfig(module_type="not", step_id="not", input_links={"a": ["and.y"]}
        nand_p_conf = PipelineConfig(doc="Returns 'False' if both inputs are 'True'.",
                            steps=[and_step, not_step],
                            input_aliases={
                                "and__a": "a",
                                "and__b": "b"
                            },
                            output_aliases={
                                "not__y": "y"
                            }}
        ```

        Or, the same thing in json:

        ``` json
        {
          "module_type_name": "nand",
          "doc": "Returns 'False' if both inputs are 'True'.",
          "steps": [
            {
              "module_type": "and",
              "step_id": "and"
            },
            {
              "module_type": "not",
              "step_id": "not",
              "input_links": {
                "a": "and.y"
              }
            }
          ],
          "input_aliases": {
            "and__a": "a",
            "and__b": "b"
          },
          "output_aliases": {
            "not__y": "y"
          }
        }
        ```
    """

    _kiara_model_id = "instance.module_config.pipeline"

    @classmethod
    def from_file(
        cls,
        path: str,
        kiara: Optional["Kiara"] = None,
        # module_map: Optional[Mapping[str, Any]] = None,
    ):

        data = get_data_from_file(path)
        pipeline_name = data.pop("pipeline_name", None)
        if pipeline_name is None:
            pipeline_name = os.path.basename(path)

        return cls.from_config(pipeline_name=pipeline_name, data=data, kiara=kiara)

    @classmethod
    def from_config(
        cls,
        pipeline_name: str,
        data: Mapping[str, Any],
        kiara: Optional["Kiara"] = None,
        # module_map: Optional[Mapping[str, Any]] = None,
    ):

        if kiara is None:
            from kiara.context import Kiara

            kiara = Kiara.instance()

        if not kiara.operation_registry.is_initialized:
            kiara.operation_registry.operations  # noqa

        config = cls._from_config(pipeline_name=pipeline_name, data=data, kiara=kiara)
        return config

    @classmethod
    def _from_config(
        cls,
        pipeline_name: str,
        data: Mapping[str, Any],
        kiara: "Kiara",
        module_map: Optional[Mapping[str, Any]] = None,
    ):

        data = dict(data)
        steps = data.pop("steps")
        steps = PipelineStep.create_steps(*steps, kiara=kiara, module_map=module_map)
        data["steps"] = steps
        if not data.get("input_aliases"):
            data["input_aliases"] = create_input_alias_map(steps)
        if not data.get("output_aliases"):
            data["output_aliases"] = create_output_alias_map(steps)

        result = cls(pipeline_name=pipeline_name, **data)

        return result

    class Config:
        extra = Extra.ignore
        validate_assignment = True

    pipeline_name: str = Field(description="The name of this pipeline.")
    steps: List[PipelineStep] = Field(
        description="A list of steps/modules of this pipeline, and their connections.",
    )
    input_aliases: Dict[str, str] = Field(
        description="A map of input aliases, with the calculated (<step_id>__<input_name> -- double underscore!) name as key, and a string (the resulting workflow input alias) as value. Check the documentation for the config class for which marker strings can be used to automatically create this map if possible.",
    )
    output_aliases: Dict[str, str] = Field(
        description="A map of output aliases, with the calculated (<step_id>__<output_name> -- double underscore!) name as key, and a string (the resulting workflow output alias) as value.  Check the documentation for the config class for which marker strings can be used to automatically create this map if possible.",
    )
    doc: str = Field(
        default="-- n/a --", description="Documentation about what the pipeline does."
    )
    context: Dict[str, Any] = Field(
        default_factory=dict, description="Metadata for this workflow."
    )
    _structure: Optional["PipelineStructure"] = PrivateAttr(default=None)

    @validator("steps", pre=True)
    def _validate_steps(cls, v):

        if not v:
            raise ValueError(f"Invalid type for 'steps' value: {type(v)}")

        steps = []
        for step in v:
            if not step:
                raise ValueError("No step data provided.")
            if isinstance(step, PipelineStep):
                steps.append(step)
            elif isinstance(step, Mapping):
                steps.append(PipelineStep(**step))
            else:
                raise TypeError(step)
        return steps

    @property
    def structure(self) -> "PipelineStructure":

        if self._structure is not None:
            return self._structure

        from kiara.models.module.pipeline.structure import PipelineStructure

        self._structure = PipelineStructure(pipeline_config=self)
        return self._structure

    def create_renderable(self, **config: Any) -> RenderableType:

        return create_table_from_model_object(self, exclude_fields={"steps"})

    # def create_input_alias_map(self) -> Dict[str, str]:
    #
    #     aliases: Dict[str, List[str]] = {}
    #     for step in self.steps:
    #         field_names = step.module.input_names
    #         for field_name in field_names:
    #             aliases.setdefault(field_name, []).append(step.step_id)
    #
    #     result: Dict[str, str] = {}
    #     for field_name, step_ids in aliases.items():
    #         for step_id in step_ids:
    #             generated = generate_pipeline_endpoint_name(step_id, field_name)
    #             result[generated] = generated
    #
    #     return result
    #
    # def create_output_alias_map(self) -> Dict[str, str]:
    #
    #     aliases: Dict[str, List[str]] = {}
    #     for step in self.steps:
    #         field_names = step.module.input_names
    #         for field_name in field_names:
    #             aliases.setdefault(field_name, []).append(step.step_id)
    #
    #     result: Dict[str, str] = {}
    #     for field_name, step_ids in aliases.items():
    #         for step_id in step_ids:
    #             generated = generate_pipeline_endpoint_name(step_id, field_name)
    #             result[generated] = generated
    #
    #     return result

Attributes

context: Dict[str, Any] pydantic-field

Metadata for this workflow.

doc: str pydantic-field

Documentation about what the pipeline does.

input_aliases: Dict[str, str] pydantic-field required

A map of input aliases, with the calculated (__ -- double underscore!) name as key, and a string (the resulting workflow input alias) as value. Check the documentation for the config class for which marker strings can be used to automatically create this map if possible.

output_aliases: Dict[str, str] pydantic-field required

A map of output aliases, with the calculated (__ -- double underscore!) name as key, and a string (the resulting workflow output alias) as value. Check the documentation for the config class for which marker strings can be used to automatically create this map if possible.

pipeline_name: str pydantic-field required

The name of this pipeline.

steps: List[kiara.models.module.pipeline.PipelineStep] pydantic-field required

A list of steps/modules of this pipeline, and their connections.

structure: PipelineStructure property readonly

Config

Source code in kiara/modules/included_core_modules/pipeline.py

class Config:
    extra = Extra.ignore
    validate_assignment = True

extra

validate_assignment

create_renderable(self, **config)

Source code in kiara/modules/included_core_modules/pipeline.py

def create_renderable(self, **config: Any) -> RenderableType:

    return create_table_from_model_object(self, exclude_fields={"steps"})

from_config(pipeline_name, data, kiara=None) classmethod

Source code in kiara/modules/included_core_modules/pipeline.py

@classmethod
def from_config(
    cls,
    pipeline_name: str,
    data: Mapping[str, Any],
    kiara: Optional["Kiara"] = None,
    # module_map: Optional[Mapping[str, Any]] = None,
):

    if kiara is None:
        from kiara.context import Kiara

        kiara = Kiara.instance()

    if not kiara.operation_registry.is_initialized:
        kiara.operation_registry.operations  # noqa

    config = cls._from_config(pipeline_name=pipeline_name, data=data, kiara=kiara)
    return config

from_file(path, kiara=None) classmethod

Source code in kiara/modules/included_core_modules/pipeline.py

@classmethod
def from_file(
    cls,
    path: str,
    kiara: Optional["Kiara"] = None,
    # module_map: Optional[Mapping[str, Any]] = None,
):

    data = get_data_from_file(path)
    pipeline_name = data.pop("pipeline_name", None)
    if pipeline_name is None:
        pipeline_name = os.path.basename(path)

    return cls.from_config(pipeline_name=pipeline_name, data=data, kiara=kiara)

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/pipeline.py

def create_inputs_schema(
    self,
) -> ValueSetSchema:

    pipeline_structure: PipelineStructure = self.config.structure
    return pipeline_structure.pipeline_inputs_schema

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/pipeline.py

def create_outputs_schema(
    self,
) -> ValueSetSchema:
    pipeline_structure: PipelineStructure = self.config.structure
    return pipeline_structure.pipeline_outputs_schema

is_pipeline() classmethod

Check whether this module type is a pipeline, or not.

Source code in kiara/modules/included_core_modules/pipeline.py

@classmethod
def is_pipeline(cls) -> bool:
    return True

process(self, inputs, outputs, job_log)

Source code in kiara/modules/included_core_modules/pipeline.py

def process(self, inputs: ValueMap, outputs: ValueMapWritable, job_log: JobLog):

    pipeline_structure: PipelineStructure = self.config.structure

    pipeline = Pipeline(
        structure=pipeline_structure, data_registry=outputs._data_registry
    )

    assert self._job_registry is not None

    controller = SinglePipelineBatchController(
        pipeline=pipeline, job_registry=self._job_registry
    )

    pipeline.set_pipeline_inputs(inputs=inputs)
    controller.process_pipeline()

    # TODO: resolve values first?
    outputs.set_values(**pipeline.get_current_pipeline_outputs())

pretty_print

Classes

PrettyPrintAnyValueModule (PrettyPrintModule)

Source code in kiara/modules/included_core_modules/pretty_print.py

class PrettyPrintAnyValueModule(PrettyPrintModule):

    _module_type_name = "pretty_print.any.value"

    def render__any__as__string(self, value: Value, render_config: Dict[str, Any]):

        data = value.data
        if isinstance(data, KiaraModel):
            return data.json(option=orjson.OPT_INDENT_2)
        else:
            return str(data)

    def render__any__as__terminal_renderable(
        self, value: Value, render_config: Dict[str, Any]
    ):

        data = value.data

        if isinstance(data, BaseModel):
            rendered = create_table_from_model_object(
                model=data, render_config=render_config
            )
        elif isinstance(data, Iterable):
            rendered = pprint.pformat(data)
        else:
            rendered = str(data)
        return rendered

render__any__as__string(self, value, render_config)

Source code in kiara/modules/included_core_modules/pretty_print.py

def render__any__as__string(self, value: Value, render_config: Dict[str, Any]):

    data = value.data
    if isinstance(data, KiaraModel):
        return data.json(option=orjson.OPT_INDENT_2)
    else:
        return str(data)

render__any__as__terminal_renderable(self, value, render_config)

Source code in kiara/modules/included_core_modules/pretty_print.py

def render__any__as__terminal_renderable(
    self, value: Value, render_config: Dict[str, Any]
):

    data = value.data

    if isinstance(data, BaseModel):
        rendered = create_table_from_model_object(
            model=data, render_config=render_config
        )
    elif isinstance(data, Iterable):
        rendered = pprint.pformat(data)
    else:
        rendered = str(data)
    return rendered

PrettyPrintConfig (KiaraModuleConfig) pydantic-model

Source code in kiara/modules/included_core_modules/pretty_print.py

class PrettyPrintConfig(KiaraModuleConfig):

    source_type: str = Field(description="The value type of the source value.")
    target_type: str = Field(description="The value type of the rendered value.")

    @validator("source_type")
    def validate_source_type(cls, value):
        if value == "render_config":
            raise ValueError(f"Invalid source type: {value}.")
        return value

Attributes

source_type: str pydantic-field required

The value type of the source value.

target_type: str pydantic-field required

The value type of the rendered value.

validate_source_type(value) classmethod

Source code in kiara/modules/included_core_modules/pretty_print.py

@validator("source_type")
def validate_source_type(cls, value):
    if value == "render_config":
        raise ValueError(f"Invalid source type: {value}.")
    return value

PrettyPrintModule (KiaraModule)

Source code in kiara/modules/included_core_modules/pretty_print.py

class PrettyPrintModule(KiaraModule):

    _module_type_name: str = None  # type: ignore
    _config_cls = PrettyPrintConfig

    @classmethod
    def retrieve_supported_render_combinations(cls) -> Iterable[Tuple[str, str]]:

        result = []
        for attr in dir(cls):
            if (
                len(attr) <= 19
                or not attr.startswith("pretty_print__")
                or "__as__" not in attr
            ):
                continue

            attr = attr[14:]
            end_start_type = attr.find("__as__")
            source_type = attr[0:end_start_type]
            target_type = attr[end_start_type + 6 :]  # noqa
            result.append((source_type, target_type))
        return result

    # def create_persistence_config_schema(self) -> Optional[Mapping[str, Mapping[str, Any]]]:
    #     return None

    def create_inputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        source_type = self.get_config_value("source_type")
        assert source_type not in ["target", "base_name"]

        schema = {
            source_type: {"type": source_type, "doc": "The value to render."},
            "render_config": {
                "type": "any",
                "doc": "Value type dependent render configuration.",
                "optional": True,
            },
        }

        return schema

    def create_outputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        return {
            "rendered_value": {
                "type": self.get_config_value("target_type"),
                "doc": "The rendered value.",
            }
        }

    def process(self, inputs: ValueMap, outputs: ValueMap):

        source_type = self.get_config_value("source_type")
        target_type = self.get_config_value("target_type")

        value = inputs.get_value_obj(source_type)
        render_config = inputs.get_value_data("render_config")

        func_name = f"pretty_print__{source_type}__as__{target_type}"

        func = getattr(self, func_name)
        # TODO: check function signature is valid
        result = func(value=value, render_config=render_config)

        outputs.set_value("rendered_value", result)

Classes

_config_cls (KiaraModuleConfig) private pydantic-model

Source code in kiara/modules/included_core_modules/pretty_print.py

class PrettyPrintConfig(KiaraModuleConfig):

    source_type: str = Field(description="The value type of the source value.")
    target_type: str = Field(description="The value type of the rendered value.")

    @validator("source_type")
    def validate_source_type(cls, value):
        if value == "render_config":
            raise ValueError(f"Invalid source type: {value}.")
        return value

Attributes

source_type: str pydantic-field required

The value type of the source value.

target_type: str pydantic-field required

The value type of the rendered value.

validate_source_type(value) classmethod

Source code in kiara/modules/included_core_modules/pretty_print.py

@validator("source_type")
def validate_source_type(cls, value):
    if value == "render_config":
        raise ValueError(f"Invalid source type: {value}.")
    return value

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/pretty_print.py

def create_inputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    source_type = self.get_config_value("source_type")
    assert source_type not in ["target", "base_name"]

    schema = {
        source_type: {"type": source_type, "doc": "The value to render."},
        "render_config": {
            "type": "any",
            "doc": "Value type dependent render configuration.",
            "optional": True,
        },
    }

    return schema

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/pretty_print.py

def create_outputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    return {
        "rendered_value": {
            "type": self.get_config_value("target_type"),
            "doc": "The rendered value.",
        }
    }

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/pretty_print.py

def process(self, inputs: ValueMap, outputs: ValueMap):

    source_type = self.get_config_value("source_type")
    target_type = self.get_config_value("target_type")

    value = inputs.get_value_obj(source_type)
    render_config = inputs.get_value_data("render_config")

    func_name = f"pretty_print__{source_type}__as__{target_type}"

    func = getattr(self, func_name)
    # TODO: check function signature is valid
    result = func(value=value, render_config=render_config)

    outputs.set_value("rendered_value", result)

retrieve_supported_render_combinations() classmethod

Source code in kiara/modules/included_core_modules/pretty_print.py

@classmethod
def retrieve_supported_render_combinations(cls) -> Iterable[Tuple[str, str]]:

    result = []
    for attr in dir(cls):
        if (
            len(attr) <= 19
            or not attr.startswith("pretty_print__")
            or "__as__" not in attr
        ):
            continue

        attr = attr[14:]
        end_start_type = attr.find("__as__")
        source_type = attr[0:end_start_type]
        target_type = attr[end_start_type + 6 :]  # noqa
        result.append((source_type, target_type))
    return result

ValueTypePrettyPrintModule (KiaraModule)

Source code in kiara/modules/included_core_modules/pretty_print.py

class ValueTypePrettyPrintModule(KiaraModule):

    _module_type_name = "pretty_print.value"
    _config_cls = PrettyPrintConfig

    def create_inputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        source_type = self.get_config_value("source_type")
        assert source_type not in ["target", "base_name"]

        schema = {
            source_type: {"type": source_type, "doc": "The value to render."},
            "render_config": {
                "type": "any",
                "doc": "Value type dependent render configuration.",
                "optional": True,
            },
        }

        return schema

    def create_outputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        return {
            "rendered_value": {
                "type": self.get_config_value("target_type"),
                "doc": "The rendered value.",
            }
        }

    def process(self, inputs: ValueMap, outputs: ValueMap):

        source_type = self.get_config_value("source_type")
        target_type = self.get_config_value("target_type")

        source_value = inputs.get_value_obj(source_type)
        render_config = inputs.get_value_obj("render_config")

        data_type_cls = source_value.data_type_class.get_class()
        data_type = data_type_cls(**source_value.value_schema.type_config)

        func_name = f"pretty_print_as__{target_type}"
        func = getattr(data_type, func_name)

        render_config_dict = render_config.data
        if render_config_dict is None:
            render_config_dict = {}

        result = func(value=source_value, render_config=render_config_dict)
        # TODO: check we have the correct type?
        outputs.set_value("rendered_value", result)

Classes

_config_cls (KiaraModuleConfig) private pydantic-model

Source code in kiara/modules/included_core_modules/pretty_print.py

class PrettyPrintConfig(KiaraModuleConfig):

    source_type: str = Field(description="The value type of the source value.")
    target_type: str = Field(description="The value type of the rendered value.")

    @validator("source_type")
    def validate_source_type(cls, value):
        if value == "render_config":
            raise ValueError(f"Invalid source type: {value}.")
        return value

Attributes

source_type: str pydantic-field required

The value type of the source value.

target_type: str pydantic-field required

The value type of the rendered value.

validate_source_type(value) classmethod

Source code in kiara/modules/included_core_modules/pretty_print.py

@validator("source_type")
def validate_source_type(cls, value):
    if value == "render_config":
        raise ValueError(f"Invalid source type: {value}.")
    return value

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/pretty_print.py

def create_inputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    source_type = self.get_config_value("source_type")
    assert source_type not in ["target", "base_name"]

    schema = {
        source_type: {"type": source_type, "doc": "The value to render."},
        "render_config": {
            "type": "any",
            "doc": "Value type dependent render configuration.",
            "optional": True,
        },
    }

    return schema

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/pretty_print.py

def create_outputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    return {
        "rendered_value": {
            "type": self.get_config_value("target_type"),
            "doc": "The rendered value.",
        }
    }

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/pretty_print.py

def process(self, inputs: ValueMap, outputs: ValueMap):

    source_type = self.get_config_value("source_type")
    target_type = self.get_config_value("target_type")

    source_value = inputs.get_value_obj(source_type)
    render_config = inputs.get_value_obj("render_config")

    data_type_cls = source_value.data_type_class.get_class()
    data_type = data_type_cls(**source_value.value_schema.type_config)

    func_name = f"pretty_print_as__{target_type}"
    func = getattr(data_type, func_name)

    render_config_dict = render_config.data
    if render_config_dict is None:
        render_config_dict = {}

    result = func(value=source_value, render_config=render_config_dict)
    # TODO: check we have the correct type?
    outputs.set_value("rendered_value", result)

render_value

Classes

RenderValueModule (KiaraModule)

Source code in kiara/modules/included_core_modules/render_value.py

class RenderValueModule(KiaraModule):

    _config_cls = RenderValueModuleConfig
    _module_type_name: str = "render.value"

    def create_inputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        instruction = self.get_config_value("render_instruction_type")
        model_registry = ModelRegistry.instance()
        instr_model_cls: Type[RenderInstruction] = model_registry.get_model_cls(instruction, required_subclass=RenderInstruction)  # type: ignore

        data_type_name = instr_model_cls.retrieve_source_type()
        assert data_type_name

        inputs = {
            data_type_name: {
                "type": data_type_name,
                "doc": f"A value of type '{data_type_name}'",
                "optional": False,
            },
            "render_instruction": {
                "type": "render_instruction",
                "doc": "Instructions/config on how (or what) to render the provided value.",
                "optional": False,
                "default": {"number_of_rows": 20, "row_offset": 0, "columns": None},
            },
        }
        return inputs

    def create_outputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        result_model_type: str = self.get_config_value("target_type")

        outputs = {
            result_model_type: {"type": result_model_type, "doc": "The rendered data."},
            "render_metadata": {
                "type": "render_metadata",
            },
        }

        return outputs

    def process(self, inputs: ValueMap, outputs: ValueMap) -> None:

        instruction_type = self.get_config_value("render_instruction_type")
        model_registry = ModelRegistry.instance()
        instr_info: TypeInfo = model_registry.all_models.get(instruction_type)  # type: ignore
        instr_model: Type[RenderInstruction] = instr_info.python_class.get_class()  # type: ignore

        data_type_name = instr_model.retrieve_source_type()

        render_instruction: RenderInstruction = inputs.get_value_data(
            "render_instruction"
        )
        if not issubclass(render_instruction.__class__, instr_model):
            raise KiaraProcessingException(
                f"Invalid type for 'render_instruction': must be a subclass of '{instr_model.__name__}'."
            )

        result_model_type: str = self.get_config_value("target_type")

        value: Value = inputs.get_value_obj(data_type_name)

        func_name = f"render_as__{result_model_type}"

        func = getattr(render_instruction, func_name)
        rendered: Union[RenderValueResult, Any] = func(value=value)
        try:
            rendered_value = rendered.rendered
            metadata = rendered.metadata
        except Exception:
            rendered_value = rendered
            metadata = None

        if not metadata:
            metadata = RenderMetadata()

        outputs.set_values(
            **{result_model_type: rendered_value, "render_metadata": metadata}
        )

Classes

_config_cls (KiaraModuleConfig) private pydantic-model

Source code in kiara/modules/included_core_modules/render_value.py

class RenderValueModuleConfig(KiaraModuleConfig):

    render_instruction_type: str = Field(
        description="The id of the model that describes (and handles) the actual rendering."
    )
    target_type: str = Field(description="The type of the rendered result.")

    @validator("render_instruction_type")
    def validate_render_instruction(cls, value: Any):

        registry = ModelRegistry.instance()

        if value not in registry.all_models.keys():
            raise ValueError(
                f"Invalid model type '{value}'. Value model ids: {', '.join(registry.all_models.keys())}."
            )

        return value

Attributes

render_instruction_type: str pydantic-field required

The id of the model that describes (and handles) the actual rendering.

target_type: str pydantic-field required

The type of the rendered result.

validate_render_instruction(value) classmethod

Source code in kiara/modules/included_core_modules/render_value.py

@validator("render_instruction_type")
def validate_render_instruction(cls, value: Any):

    registry = ModelRegistry.instance()

    if value not in registry.all_models.keys():
        raise ValueError(
            f"Invalid model type '{value}'. Value model ids: {', '.join(registry.all_models.keys())}."
        )

    return value

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/render_value.py

def create_inputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    instruction = self.get_config_value("render_instruction_type")
    model_registry = ModelRegistry.instance()
    instr_model_cls: Type[RenderInstruction] = model_registry.get_model_cls(instruction, required_subclass=RenderInstruction)  # type: ignore

    data_type_name = instr_model_cls.retrieve_source_type()
    assert data_type_name

    inputs = {
        data_type_name: {
            "type": data_type_name,
            "doc": f"A value of type '{data_type_name}'",
            "optional": False,
        },
        "render_instruction": {
            "type": "render_instruction",
            "doc": "Instructions/config on how (or what) to render the provided value.",
            "optional": False,
            "default": {"number_of_rows": 20, "row_offset": 0, "columns": None},
        },
    }
    return inputs

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/render_value.py

def create_outputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    result_model_type: str = self.get_config_value("target_type")

    outputs = {
        result_model_type: {"type": result_model_type, "doc": "The rendered data."},
        "render_metadata": {
            "type": "render_metadata",
        },
    }

    return outputs

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/render_value.py

def process(self, inputs: ValueMap, outputs: ValueMap) -> None:

    instruction_type = self.get_config_value("render_instruction_type")
    model_registry = ModelRegistry.instance()
    instr_info: TypeInfo = model_registry.all_models.get(instruction_type)  # type: ignore
    instr_model: Type[RenderInstruction] = instr_info.python_class.get_class()  # type: ignore

    data_type_name = instr_model.retrieve_source_type()

    render_instruction: RenderInstruction = inputs.get_value_data(
        "render_instruction"
    )
    if not issubclass(render_instruction.__class__, instr_model):
        raise KiaraProcessingException(
            f"Invalid type for 'render_instruction': must be a subclass of '{instr_model.__name__}'."
        )

    result_model_type: str = self.get_config_value("target_type")

    value: Value = inputs.get_value_obj(data_type_name)

    func_name = f"render_as__{result_model_type}"

    func = getattr(render_instruction, func_name)
    rendered: Union[RenderValueResult, Any] = func(value=value)
    try:
        rendered_value = rendered.rendered
        metadata = rendered.metadata
    except Exception:
        rendered_value = rendered
        metadata = None

    if not metadata:
        metadata = RenderMetadata()

    outputs.set_values(
        **{result_model_type: rendered_value, "render_metadata": metadata}
    )

RenderValueModuleConfig (KiaraModuleConfig) pydantic-model

Source code in kiara/modules/included_core_modules/render_value.py

class RenderValueModuleConfig(KiaraModuleConfig):

    render_instruction_type: str = Field(
        description="The id of the model that describes (and handles) the actual rendering."
    )
    target_type: str = Field(description="The type of the rendered result.")

    @validator("render_instruction_type")
    def validate_render_instruction(cls, value: Any):

        registry = ModelRegistry.instance()

        if value not in registry.all_models.keys():
            raise ValueError(
                f"Invalid model type '{value}'. Value model ids: {', '.join(registry.all_models.keys())}."
            )

        return value

Attributes

render_instruction_type: str pydantic-field required

The id of the model that describes (and handles) the actual rendering.

target_type: str pydantic-field required

The type of the rendered result.

validate_render_instruction(value) classmethod

Source code in kiara/modules/included_core_modules/render_value.py

@validator("render_instruction_type")
def validate_render_instruction(cls, value: Any):

    registry = ModelRegistry.instance()

    if value not in registry.all_models.keys():
        raise ValueError(
            f"Invalid model type '{value}'. Value model ids: {', '.join(registry.all_models.keys())}."
        )

    return value

serialization

Classes

DeserializeFromJsonModule (KiaraModule)

Source code in kiara/modules/included_core_modules/serialization.py

class DeserializeFromJsonModule(KiaraModule):

    _module_type_name: str = "deserialize.from_json"
    _config_cls = DeserializeJsonConfig

    def create_inputs_schema(
        self,
    ) -> ValueSetSchema:

        return {
            "value": {
                "type": "any",
                "doc": "The value object to deserialize the data for.",
            }
        }

    def create_outputs_schema(
        self,
    ) -> ValueSetSchema:

        return {
            "python_object": {
                "type": "python_object",
                "doc": "The deserialized python object.",
            }
        }

    def process(self, inputs: ValueMap, outputs: ValueMap):

        value: Value = inputs.get_value_obj("value")
        serialized: SerializedData = value.serialized_data

        chunks = serialized.get_serialized_data(self.get_config_value("result_path"))
        assert chunks.get_number_of_chunks() == 1
        _data = list(chunks.get_chunks(as_files=False))
        assert len(_data) == 1
        _chunk: bytes = _data[0]  # type: ignore

        deserialized = orjson.loads(_chunk)

        outputs.set_value("python_object", deserialized)

Classes

_config_cls (KiaraModuleConfig) private pydantic-model

Source code in kiara/modules/included_core_modules/serialization.py

class DeserializeJsonConfig(KiaraModuleConfig):

    result_path: Optional[str] = Field(
        description="The path of the result dictionary to return.", default="data"
    )

Attributes

result_path: str pydantic-field

The path of the result dictionary to return.

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/serialization.py

def create_inputs_schema(
    self,
) -> ValueSetSchema:

    return {
        "value": {
            "type": "any",
            "doc": "The value object to deserialize the data for.",
        }
    }

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/serialization.py

def create_outputs_schema(
    self,
) -> ValueSetSchema:

    return {
        "python_object": {
            "type": "python_object",
            "doc": "The deserialized python object.",
        }
    }

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/serialization.py

def process(self, inputs: ValueMap, outputs: ValueMap):

    value: Value = inputs.get_value_obj("value")
    serialized: SerializedData = value.serialized_data

    chunks = serialized.get_serialized_data(self.get_config_value("result_path"))
    assert chunks.get_number_of_chunks() == 1
    _data = list(chunks.get_chunks(as_files=False))
    assert len(_data) == 1
    _chunk: bytes = _data[0]  # type: ignore

    deserialized = orjson.loads(_chunk)

    outputs.set_value("python_object", deserialized)

DeserializeJsonConfig (KiaraModuleConfig) pydantic-model

Source code in kiara/modules/included_core_modules/serialization.py

class DeserializeJsonConfig(KiaraModuleConfig):

    result_path: Optional[str] = Field(
        description="The path of the result dictionary to return.", default="data"
    )

Attributes

result_path: str pydantic-field

The path of the result dictionary to return.

DeserializeValueModule (KiaraModule)

Source code in kiara/modules/included_core_modules/serialization.py

class DeserializeValueModule(KiaraModule):

    _config_cls = SerializeConfig

    @classmethod
    @abc.abstractmethod
    def retrieve_serialized_value_type(cls) -> str:
        raise NotImplementedError()

    @classmethod
    @abc.abstractmethod
    def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
        raise NotImplementedError()

    @classmethod
    @abc.abstractmethod
    def retrieve_supported_serialization_profile(cls) -> str:
        raise NotImplementedError()

    def create_inputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        value_type = self.get_config_value("value_type")
        return {
            value_type: {
                "type": value_type,
                "doc": "The value object.",
            },
            "deserialization_config": {
                "type": "any",
                "doc": "Serialization-format specific configuration.",
                "optional": True,
            },
        }

    def create_outputs_schema(
        self,
    ) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

        return {
            "python_object": {
                "type": "python_object",
                "doc": "The deserialized python object instance.",
            },
        }

    def process(self, inputs: ValueMap, outputs: ValueMap) -> None:

        value_type = self.get_config_value("value_type")
        serialized_value = inputs.get_value_obj(value_type)
        config = inputs.get_value_obj("deserialization_config")

        target_profile = self.get_config_value("target_profile")
        func_name = f"to__{target_profile}"
        func = getattr(self, func_name)

        if config.is_set:
            _config = config.data
        else:
            _config = {}

        result: Any = func(data=serialized_value.serialized_data, **_config)
        outputs.set_value("python_object", result)

Classes

_config_cls (KiaraModuleConfig) private pydantic-model

Source code in kiara/modules/included_core_modules/serialization.py

class SerializeConfig(KiaraModuleConfig):

    value_type: str = Field(
        description="The value type of the actual (unserialized) value."
    )
    target_profile: str = Field(
        description="The profile name of the de-serialization result data."
    )
    serialization_profile: str = Field(
        description="The name of the serialization profile used to serialize the source value."
    )

    @validator("value_type")
    def validate_source_type(cls, value):
        if value == "serialization_config":
            raise ValueError(f"Invalid source type: {value}.")
        return value

Attributes

serialization_profile: str pydantic-field required

The name of the serialization profile used to serialize the source value.

target_profile: str pydantic-field required

The profile name of the de-serialization result data.

value_type: str pydantic-field required

The value type of the actual (unserialized) value.

validate_source_type(value) classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@validator("value_type")
def validate_source_type(cls, value):
    if value == "serialization_config":
        raise ValueError(f"Invalid source type: {value}.")
    return value

Methods

create_inputs_schema(self)

Return the schema for this types' inputs.

Source code in kiara/modules/included_core_modules/serialization.py

def create_inputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    value_type = self.get_config_value("value_type")
    return {
        value_type: {
            "type": value_type,
            "doc": "The value object.",
        },
        "deserialization_config": {
            "type": "any",
            "doc": "Serialization-format specific configuration.",
            "optional": True,
        },
    }

create_outputs_schema(self)

Return the schema for this types' outputs.

Source code in kiara/modules/included_core_modules/serialization.py

def create_outputs_schema(
    self,
) -> Mapping[str, Union[ValueSchema, Mapping[str, Any]]]:

    return {
        "python_object": {
            "type": "python_object",
            "doc": "The deserialized python object instance.",
        },
    }

process(self, inputs, outputs)

Source code in kiara/modules/included_core_modules/serialization.py

def process(self, inputs: ValueMap, outputs: ValueMap) -> None:

    value_type = self.get_config_value("value_type")
    serialized_value = inputs.get_value_obj(value_type)
    config = inputs.get_value_obj("deserialization_config")

    target_profile = self.get_config_value("target_profile")
    func_name = f"to__{target_profile}"
    func = getattr(self, func_name)

    if config.is_set:
        _config = config.data
    else:
        _config = {}

    result: Any = func(data=serialized_value.serialized_data, **_config)
    outputs.set_value("python_object", result)

retrieve_serialized_value_type() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
@abc.abstractmethod
def retrieve_serialized_value_type(cls) -> str:
    raise NotImplementedError()

retrieve_supported_serialization_profile() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
@abc.abstractmethod
def retrieve_supported_serialization_profile(cls) -> str:
    raise NotImplementedError()

retrieve_supported_target_profiles() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
@abc.abstractmethod
def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
    raise NotImplementedError()

LoadBytesModule (DeserializeValueModule)

Source code in kiara/modules/included_core_modules/serialization.py

class LoadBytesModule(DeserializeValueModule):

    _module_type_name = "load.bytes"

    @classmethod
    def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
        return {"python_object": bytes}

    @classmethod
    def retrieve_supported_serialization_profile(cls) -> str:
        return "raw"

    @classmethod
    def retrieve_serialized_value_type(cls) -> str:
        return "bytes"

    def to__python_object(self, data: SerializedData, **config: Any) -> bytes:

        chunks = data.get_serialized_data("bytes")
        assert chunks.get_number_of_chunks() == 1
        _chunks = list(chunks.get_chunks(as_files=False))
        assert len(_chunks) == 1
        _chunk: bytes = _chunks[0]  # type: ignore
        return _chunk

retrieve_serialized_value_type() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_serialized_value_type(cls) -> str:
    return "bytes"

retrieve_supported_serialization_profile() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_supported_serialization_profile(cls) -> str:
    return "raw"

retrieve_supported_target_profiles() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
    return {"python_object": bytes}

to__python_object(self, data, **config)

Source code in kiara/modules/included_core_modules/serialization.py

def to__python_object(self, data: SerializedData, **config: Any) -> bytes:

    chunks = data.get_serialized_data("bytes")
    assert chunks.get_number_of_chunks() == 1
    _chunks = list(chunks.get_chunks(as_files=False))
    assert len(_chunks) == 1
    _chunk: bytes = _chunks[0]  # type: ignore
    return _chunk

LoadInternalModel (DeserializeValueModule)

Source code in kiara/modules/included_core_modules/serialization.py

class LoadInternalModel(DeserializeValueModule):

    _module_type_name = "load.internal_model"

    @classmethod
    def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
        return {"python_object": KiaraModel}

    @classmethod
    def retrieve_supported_serialization_profile(cls) -> str:
        return "json"

    @classmethod
    def retrieve_serialized_value_type(cls) -> str:
        return "internal_model"

    def to__python_object(self, data: SerializedData, **config: Any) -> KiaraModel:

        chunks = data.get_serialized_data("data")
        assert chunks.get_number_of_chunks() == 1
        _chunks = list(chunks.get_chunks(as_files=False))
        assert len(_chunks) == 1

        bytes_string: bytes = _chunks[0]  # type: ignore
        model_data = orjson.loads(bytes_string)

        model_id: str = data.data_type_config["kiara_model_id"]
        model_registry = ModelRegistry.instance()
        m_cls = model_registry.get_model_cls(kiara_model_id=model_id)
        obj = m_cls(**model_data)
        return obj

retrieve_serialized_value_type() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_serialized_value_type(cls) -> str:
    return "internal_model"

retrieve_supported_serialization_profile() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_supported_serialization_profile(cls) -> str:
    return "json"

retrieve_supported_target_profiles() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
    return {"python_object": KiaraModel}

to__python_object(self, data, **config)

Source code in kiara/modules/included_core_modules/serialization.py

def to__python_object(self, data: SerializedData, **config: Any) -> KiaraModel:

    chunks = data.get_serialized_data("data")
    assert chunks.get_number_of_chunks() == 1
    _chunks = list(chunks.get_chunks(as_files=False))
    assert len(_chunks) == 1

    bytes_string: bytes = _chunks[0]  # type: ignore
    model_data = orjson.loads(bytes_string)

    model_id: str = data.data_type_config["kiara_model_id"]
    model_registry = ModelRegistry.instance()
    m_cls = model_registry.get_model_cls(kiara_model_id=model_id)
    obj = m_cls(**model_data)
    return obj

LoadStringModule (DeserializeValueModule)

Source code in kiara/modules/included_core_modules/serialization.py

class LoadStringModule(DeserializeValueModule):

    _module_type_name = "load.string"

    @classmethod
    def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
        return {"python_object": str}

    @classmethod
    def retrieve_supported_serialization_profile(cls) -> str:
        return "raw"

    @classmethod
    def retrieve_serialized_value_type(cls) -> str:
        return "string"

    def to__python_object(self, data: SerializedData, **config: Any) -> str:

        chunks = data.get_serialized_data("string")
        assert chunks.get_number_of_chunks() == 1
        _chunks = list(chunks.get_chunks(as_files=False))
        assert len(_chunks) == 1

        bytes_string: bytes = _chunks[0]  # type: ignore
        return bytes_string.decode("utf-8")

retrieve_serialized_value_type() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_serialized_value_type(cls) -> str:
    return "string"

retrieve_supported_serialization_profile() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_supported_serialization_profile(cls) -> str:
    return "raw"

retrieve_supported_target_profiles() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:
    return {"python_object": str}

to__python_object(self, data, **config)

Source code in kiara/modules/included_core_modules/serialization.py

def to__python_object(self, data: SerializedData, **config: Any) -> str:

    chunks = data.get_serialized_data("string")
    assert chunks.get_number_of_chunks() == 1
    _chunks = list(chunks.get_chunks(as_files=False))
    assert len(_chunks) == 1

    bytes_string: bytes = _chunks[0]  # type: ignore
    return bytes_string.decode("utf-8")

SerializeConfig (KiaraModuleConfig) pydantic-model

Source code in kiara/modules/included_core_modules/serialization.py

class SerializeConfig(KiaraModuleConfig):

    value_type: str = Field(
        description="The value type of the actual (unserialized) value."
    )
    target_profile: str = Field(
        description="The profile name of the de-serialization result data."
    )
    serialization_profile: str = Field(
        description="The name of the serialization profile used to serialize the source value."
    )

    @validator("value_type")
    def validate_source_type(cls, value):
        if value == "serialization_config":
            raise ValueError(f"Invalid source type: {value}.")
        return value

Attributes

serialization_profile: str pydantic-field required

The name of the serialization profile used to serialize the source value.

target_profile: str pydantic-field required

The profile name of the de-serialization result data.

value_type: str pydantic-field required

The value type of the actual (unserialized) value.

validate_source_type(value) classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@validator("value_type")
def validate_source_type(cls, value):
    if value == "serialization_config":
        raise ValueError(f"Invalid source type: {value}.")
    return value

UnpickleModule (DeserializeValueModule)

Source code in kiara/modules/included_core_modules/serialization.py

class UnpickleModule(DeserializeValueModule):

    _module_type_name = "unpickle.value"

    @classmethod
    def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:

        return {"python_object": object}

    @classmethod
    def retrieve_supported_serialization_profile(cls) -> str:
        return "pickle"

    @classmethod
    def retrieve_serialized_value_type(cls) -> str:

        return "any"

    def to__python_object(self, data: SerializedData, **config: Any):

        try:
            import pickle5 as pickle
        except Exception:
            import pickle  # type: ignore

        assert "python_object" in data.get_keys()
        python_object_data = data.get_serialized_data("python_object")
        assert python_object_data.get_number_of_chunks() == 1

        _bytes = list(python_object_data.get_chunks(as_files=False))[0]
        data = pickle.loads(_bytes)

        return data

retrieve_serialized_value_type() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_serialized_value_type(cls) -> str:

    return "any"

retrieve_supported_serialization_profile() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_supported_serialization_profile(cls) -> str:
    return "pickle"

retrieve_supported_target_profiles() classmethod

Source code in kiara/modules/included_core_modules/serialization.py

@classmethod
def retrieve_supported_target_profiles(cls) -> Mapping[str, Type]:

    return {"python_object": object}

to__python_object(self, data, **config)

Source code in kiara/modules/included_core_modules/serialization.py

def to__python_object(self, data: SerializedData, **config: Any):

    try:
        import pickle5 as pickle
    except Exception:
        import pickle  # type: ignore

    assert "python_object" in data.get_keys()
    python_object_data = data.get_serialized_data("python_object")
    assert python_object_data.get_number_of_chunks() == 1

    _bytes = list(python_object_data.get_chunks(as_files=False))[0]
    data = pickle.loads(_bytes)

    return data

operations special

OPERATION_TYPE_DETAILS

Classes

OperationType (ABC, Generic)

Source code in kiara/operations/__init__.py

class OperationType(abc.ABC, Generic[OPERATION_TYPE_DETAILS]):
    def __init__(self, kiara: "Kiara", op_type_name: str):
        self._kiara: Kiara = kiara
        self._op_type_name: str = op_type_name

    @property
    def operations(self) -> Mapping[str, Operation]:
        return {
            op_id: self._kiara.operation_registry.get_operation(op_id)
            for op_id in self._kiara.operation_registry.operations_by_type[
                self._op_type_name
            ]
        }

    def retrieve_included_operation_configs(
        self,
    ) -> Iterable[Union[Mapping, OperationConfig]]:
        return []

    @abc.abstractmethod
    def check_matching_operation(
        self, module: "KiaraModule"
    ) -> Optional[OPERATION_TYPE_DETAILS]:
        """Check whether the provided module is a valid operation for this type."""

    def retrieve_operation_details(
        self, operation: Union[Operation, str]
    ) -> OPERATION_TYPE_DETAILS:
        """Retrieve operation details for provided operation.

        This is really just a utility method, to make the type checker happy.
        """

        if isinstance(operation, str):
            operation = self.operations[operation]

        return operation.operation_details  # type: ignore

operations: Mapping[str, kiara.models.module.operation.Operation] property readonly

Methods

check_matching_operation(self, module)

Check whether the provided module is a valid operation for this type.

Source code in kiara/operations/__init__.py

@abc.abstractmethod
def check_matching_operation(
    self, module: "KiaraModule"
) -> Optional[OPERATION_TYPE_DETAILS]:
    """Check whether the provided module is a valid operation for this type."""

retrieve_included_operation_configs(self)

Source code in kiara/operations/__init__.py

def retrieve_included_operation_configs(
    self,
) -> Iterable[Union[Mapping, OperationConfig]]:
    return []

retrieve_operation_details(self, operation)

Retrieve operation details for provided operation.

This is really just a utility method, to make the type checker happy.

Source code in kiara/operations/__init__.py

def retrieve_operation_details(
    self, operation: Union[Operation, str]
) -> OPERATION_TYPE_DETAILS:
    """Retrieve operation details for provided operation.

    This is really just a utility method, to make the type checker happy.
    """

    if isinstance(operation, str):
        operation = self.operations[operation]

    return operation.operation_details  # type: ignore

Modules

included_core_operations special

logger

Classes

CustomModuleOperationDetails (OperationDetails) pydantic-model

Source code in kiara/operations/included_core_operations/__init__.py

class CustomModuleOperationDetails(OperationDetails):
    @classmethod
    def create_from_module(cls, module: KiaraModule):

        return CustomModuleOperationDetails(
            operation_id=module.module_type_name,
            module_inputs_schema=module.inputs_schema,
            module_outputs_schema=module.outputs_schema,
        )

    module_inputs_schema: Mapping[str, ValueSchema] = Field(
        description="The input schemas of the module."
    )
    module_outputs_schema: Mapping[str, ValueSchema] = Field(
        description="The output schemas of the module."
    )
    _op_schema: OperationSchema = PrivateAttr(default=None)

    def get_operation_schema(self) -> OperationSchema:

        if self._op_schema is not None:
            return self._op_schema

        self._op_schema = OperationSchema(
            alias=self.operation_id,
            inputs_schema=self.module_inputs_schema,
            outputs_schema=self.module_outputs_schema,
        )
        return self._op_schema

    def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
        return inputs

    def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:
        return outputs

Attributes

module_inputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

The input schemas of the module.

module_outputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

The output schemas of the module.

create_from_module(module) classmethod

Source code in kiara/operations/included_core_operations/__init__.py

@classmethod
def create_from_module(cls, module: KiaraModule):

    return CustomModuleOperationDetails(
        operation_id=module.module_type_name,
        module_inputs_schema=module.inputs_schema,
        module_outputs_schema=module.outputs_schema,
    )

create_module_inputs(self, inputs)

Source code in kiara/operations/included_core_operations/__init__.py

def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
    return inputs

create_operation_outputs(self, outputs)

Source code in kiara/operations/included_core_operations/__init__.py

def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:
    return outputs

get_operation_schema(self)

Source code in kiara/operations/included_core_operations/__init__.py

def get_operation_schema(self) -> OperationSchema:

    if self._op_schema is not None:
        return self._op_schema

    self._op_schema = OperationSchema(
        alias=self.operation_id,
        inputs_schema=self.module_inputs_schema,
        outputs_schema=self.module_outputs_schema,
    )
    return self._op_schema

CustomModuleOperationType (OperationType)

Source code in kiara/operations/included_core_operations/__init__.py

class CustomModuleOperationType(OperationType[CustomModuleOperationDetails]):

    _operation_type_name = "custom_module"

    def retrieve_included_operation_configs(
        self,
    ) -> Iterable[Union[Mapping, OperationConfig]]:

        result = []
        for name, module_cls in self._kiara.module_type_classes.items():
            mod_conf = module_cls._config_cls
            if mod_conf.requires_config():
                logger.debug(
                    "ignore.custom_operation",
                    module_type=name,
                    reason="config required",
                )
                continue
            doc = DocumentationMetadataModel.from_class_doc(module_cls)
            oc = ManifestOperationConfig(module_type=name, doc=doc)
            result.append(oc)
        return result

    def check_matching_operation(
        self, module: "KiaraModule"
    ) -> Optional[CustomModuleOperationDetails]:
        mod_conf = module.__class__._config_cls

        if not mod_conf.requires_config():
            is_internal = module.characteristics.is_internal
            op_details = CustomModuleOperationDetails.create_operation_details(
                operation_id=module.module_type_name,
                module_inputs_schema=module.inputs_schema,
                module_outputs_schema=module.outputs_schema,
                is_internal_operation=is_internal,
            )
            return op_details
        else:
            return None

Methods

check_matching_operation(self, module)

Check whether the provided module is a valid operation for this type.

Source code in kiara/operations/included_core_operations/__init__.py

def check_matching_operation(
    self, module: "KiaraModule"
) -> Optional[CustomModuleOperationDetails]:
    mod_conf = module.__class__._config_cls

    if not mod_conf.requires_config():
        is_internal = module.characteristics.is_internal
        op_details = CustomModuleOperationDetails.create_operation_details(
            operation_id=module.module_type_name,
            module_inputs_schema=module.inputs_schema,
            module_outputs_schema=module.outputs_schema,
            is_internal_operation=is_internal,
        )
        return op_details
    else:
        return None

retrieve_included_operation_configs(self)

Source code in kiara/operations/included_core_operations/__init__.py

def retrieve_included_operation_configs(
    self,
) -> Iterable[Union[Mapping, OperationConfig]]:

    result = []
    for name, module_cls in self._kiara.module_type_classes.items():
        mod_conf = module_cls._config_cls
        if mod_conf.requires_config():
            logger.debug(
                "ignore.custom_operation",
                module_type=name,
                reason="config required",
            )
            continue
        doc = DocumentationMetadataModel.from_class_doc(module_cls)
        oc = ManifestOperationConfig(module_type=name, doc=doc)
        result.append(oc)
    return result

Modules

create_from

logger

Classes

CreateFromOperationType (OperationType)

Source code in kiara/operations/included_core_operations/create_from.py

class CreateFromOperationType(OperationType[CreateValueFromDetails]):

    _operation_type_name = "create_from"

    def _calculate_op_id(self, source_type: str, target_type: str):

        if source_type == "any":
            operation_id = f"create.{target_type}"
        else:
            operation_id = f"create.{target_type}.from.{source_type}"

        return operation_id

    def retrieve_included_operation_configs(
        self,
    ) -> Iterable[Union[Mapping, OperationConfig]]:

        result = {}
        for name, module_cls in self._kiara.module_type_classes.items():
            if not hasattr(module_cls, "retrieve_supported_create_combinations"):
                continue

            try:
                supported_combinations = module_cls.retrieve_supported_create_combinations()  # type: ignore
                for sup_comb in supported_combinations:
                    source_type = sup_comb["source_type"]
                    target_type = sup_comb["target_type"]
                    func = sup_comb["func"]

                    if source_type not in self._kiara.data_type_names:
                        logger.debug(
                            "ignore.operation_config",
                            module_type=name,
                            reason=f"Source type '{source_type}' not registered.",
                        )
                        continue
                    if target_type not in self._kiara.data_type_names:
                        logger.debug(
                            "ignore.operation_config",
                            module_type=name,
                            reason=f"Target type '{target_type}' not registered.",
                        )
                        continue
                    if not hasattr(module_cls, func):
                        logger.debug(
                            "ignore.operation_config",
                            module_type=name,
                            reason=f"Specified create function '{func}' not available.",
                        )
                        continue

                    mc = {"source_type": source_type, "target_type": target_type}
                    # TODO: check whether module config actually supports those, for now, only 'CreateFromModule' subtypes are supported
                    _func = getattr(module_cls, func)
                    doc = DocumentationMetadataModel.from_function(_func)

                    oc = ManifestOperationConfig(
                        module_type=name, module_config=mc, doc=doc
                    )
                    op_id = self._calculate_op_id(
                        source_type=source_type, target_type=target_type
                    )
                    result[op_id] = oc
            except Exception as e:
                if is_debug():
                    import traceback

                    traceback.print_exc()
                logger.debug(
                    "ignore.create_operation_instance", module_type=name, reason=e
                )
                continue

        return result.values()

    def check_matching_operation(
        self, module: "KiaraModule"
    ) -> Optional[CreateValueFromDetails]:

        if not isinstance(module, CreateFromModule):
            return None

        source_type = None
        for field_name, schema in module.inputs_schema.items():
            if field_name == schema.type:
                if source_type is not None:
                    logger.debug(
                        "ignore.operation",
                        operation_type="create_from",
                        reason=f"more than one possible target type field: {field_name}",
                    )
                    return None
                source_type = field_name

        if source_type is None:
            return None

        target_type = None
        for field_name, schema in module.outputs_schema.items():
            if field_name == schema.type:
                if target_type is not None:
                    logger.debug(
                        "ignore.operation",
                        operation_type="create_from",
                        reason=f"more than one possible target type field: {field_name}",
                    )
                    return None
                target_type = field_name

        if target_type is None:
            return None

        op_id = self._calculate_op_id(source_type=source_type, target_type=target_type)

        if (
            "any" in self._kiara.type_registry.get_type_lineage(target_type)
            and target_type != "any"
        ):
            is_internal = False
        else:
            is_internal = True

        optional = {}
        for field, schema in module.inputs_schema.items():
            if field == source_type:
                continue
            optional[field] = schema

        details = {
            "operation_id": op_id,
            "source_type": source_type,
            "target_type": target_type,
            "optional_args": optional,
            "is_internal_operation": is_internal,
        }

        result = CreateValueFromDetails.create_operation_details(**details)
        return result

Methods

check_matching_operation(self, module)

Check whether the provided module is a valid operation for this type.

Source code in kiara/operations/included_core_operations/create_from.py

def check_matching_operation(
    self, module: "KiaraModule"
) -> Optional[CreateValueFromDetails]:

    if not isinstance(module, CreateFromModule):
        return None

    source_type = None
    for field_name, schema in module.inputs_schema.items():
        if field_name == schema.type:
            if source_type is not None:
                logger.debug(
                    "ignore.operation",
                    operation_type="create_from",
                    reason=f"more than one possible target type field: {field_name}",
                )
                return None
            source_type = field_name

    if source_type is None:
        return None

    target_type = None
    for field_name, schema in module.outputs_schema.items():
        if field_name == schema.type:
            if target_type is not None:
                logger.debug(
                    "ignore.operation",
                    operation_type="create_from",
                    reason=f"more than one possible target type field: {field_name}",
                )
                return None
            target_type = field_name

    if target_type is None:
        return None

    op_id = self._calculate_op_id(source_type=source_type, target_type=target_type)

    if (
        "any" in self._kiara.type_registry.get_type_lineage(target_type)
        and target_type != "any"
    ):
        is_internal = False
    else:
        is_internal = True

    optional = {}
    for field, schema in module.inputs_schema.items():
        if field == source_type:
            continue
        optional[field] = schema

    details = {
        "operation_id": op_id,
        "source_type": source_type,
        "target_type": target_type,
        "optional_args": optional,
        "is_internal_operation": is_internal,
    }

    result = CreateValueFromDetails.create_operation_details(**details)
    return result

retrieve_included_operation_configs(self)

Source code in kiara/operations/included_core_operations/create_from.py

def retrieve_included_operation_configs(
    self,
) -> Iterable[Union[Mapping, OperationConfig]]:

    result = {}
    for name, module_cls in self._kiara.module_type_classes.items():
        if not hasattr(module_cls, "retrieve_supported_create_combinations"):
            continue

        try:
            supported_combinations = module_cls.retrieve_supported_create_combinations()  # type: ignore
            for sup_comb in supported_combinations:
                source_type = sup_comb["source_type"]
                target_type = sup_comb["target_type"]
                func = sup_comb["func"]

                if source_type not in self._kiara.data_type_names:
                    logger.debug(
                        "ignore.operation_config",
                        module_type=name,
                        reason=f"Source type '{source_type}' not registered.",
                    )
                    continue
                if target_type not in self._kiara.data_type_names:
                    logger.debug(
                        "ignore.operation_config",
                        module_type=name,
                        reason=f"Target type '{target_type}' not registered.",
                    )
                    continue
                if not hasattr(module_cls, func):
                    logger.debug(
                        "ignore.operation_config",
                        module_type=name,
                        reason=f"Specified create function '{func}' not available.",
                    )
                    continue

                mc = {"source_type": source_type, "target_type": target_type}
                # TODO: check whether module config actually supports those, for now, only 'CreateFromModule' subtypes are supported
                _func = getattr(module_cls, func)
                doc = DocumentationMetadataModel.from_function(_func)

                oc = ManifestOperationConfig(
                    module_type=name, module_config=mc, doc=doc
                )
                op_id = self._calculate_op_id(
                    source_type=source_type, target_type=target_type
                )
                result[op_id] = oc
        except Exception as e:
            if is_debug():
                import traceback

                traceback.print_exc()
            logger.debug(
                "ignore.create_operation_instance", module_type=name, reason=e
            )
            continue

    return result.values()

CreateValueFromDetails (BaseOperationDetails) pydantic-model

Source code in kiara/operations/included_core_operations/create_from.py

class CreateValueFromDetails(BaseOperationDetails):

    source_type: str = Field(description="The type of the value to be created.")
    target_type: str = Field(description="The result type.")
    optional_args: Mapping[str, ValueSchema] = Field(description="Optional arguments.")

    def retrieve_inputs_schema(self) -> ValueSetSchema:

        result: Dict[str, Union[ValueSchema, Dict[str, Any]]] = {
            self.source_type: {"type": self.source_type, "doc": "The source value."},
        }
        for field, schema in self.optional_args.items():
            if field in result.keys():
                raise Exception(
                    f"Can't create 'create_from' operation '{self.source_type}' -> '{self.target_type}': duplicate input field '{field}'."
                )
            result[field] = schema
        return result

    def retrieve_outputs_schema(self) -> ValueSetSchema:

        return {
            self.target_type: {"type": self.target_type, "doc": "The result value."}
        }

    def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:

        return inputs

    def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:

        return outputs

Attributes

optional_args: Mapping[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

Optional arguments.

source_type: str pydantic-field required

The type of the value to be created.

target_type: str pydantic-field required

The result type.

create_module_inputs(self, inputs)

Source code in kiara/operations/included_core_operations/create_from.py

def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:

    return inputs

create_operation_outputs(self, outputs)

Source code in kiara/operations/included_core_operations/create_from.py

def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:

    return outputs

retrieve_inputs_schema(self)

Source code in kiara/operations/included_core_operations/create_from.py

def retrieve_inputs_schema(self) -> ValueSetSchema:

    result: Dict[str, Union[ValueSchema, Dict[str, Any]]] = {
        self.source_type: {"type": self.source_type, "doc": "The source value."},
    }
    for field, schema in self.optional_args.items():
        if field in result.keys():
            raise Exception(
                f"Can't create 'create_from' operation '{self.source_type}' -> '{self.target_type}': duplicate input field '{field}'."
            )
        result[field] = schema
    return result

retrieve_outputs_schema(self)

Source code in kiara/operations/included_core_operations/create_from.py

def retrieve_outputs_schema(self) -> ValueSetSchema:

    return {
        self.target_type: {"type": self.target_type, "doc": "The result value."}
    }

metadata

Classes

ExtractMetadataDetails (BaseOperationDetails) pydantic-model

A model that contains information needed to describe an 'extract_metadata' operation.

Source code in kiara/operations/included_core_operations/metadata.py

class ExtractMetadataDetails(BaseOperationDetails):
    """A model that contains information needed to describe an 'extract_metadata' operation."""

    data_type: str = Field(
        description="The data type this metadata operation can be used with."
    )
    metadata_key: str = Field(description="The metadata key.")
    input_field_name: str = Field(description="The input field name.")
    result_field_name: str = Field(description="The result field name.")

    def retrieve_inputs_schema(self) -> ValueSetSchema:
        return {
            "value": {
                "type": self.data_type,
                "doc": f"The {self.data_type} value to extract metadata from.",
            }
        }

    def retrieve_outputs_schema(self) -> ValueSetSchema:

        return {"value_metadata": {"type": "value_metadata", "doc": "The metadata."}}

    def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
        return {self.input_field_name: inputs["value"]}

    def create_operation_outputs(self, outputs: ValueMap) -> ValueMap:

        return outputs

Attributes

data_type: str pydantic-field required

The data type this metadata operation can be used with.

input_field_name: str pydantic-field required

The input field name.

metadata_key: str pydantic-field required

The metadata key.

result_field_name: str pydantic-field required

The result field name.

create_module_inputs(self, inputs)

Source code in kiara/operations/included_core_operations/metadata.py

def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
    return {self.input_field_name: inputs["value"]}

create_operation_outputs(self, outputs)

Source code in kiara/operations/included_core_operations/metadata.py

def create_operation_outputs(self, outputs: ValueMap) -> ValueMap:

    return outputs

retrieve_inputs_schema(self)

Source code in kiara/operations/included_core_operations/metadata.py

def retrieve_inputs_schema(self) -> ValueSetSchema:
    return {
        "value": {
            "type": self.data_type,
            "doc": f"The {self.data_type} value to extract metadata from.",
        }
    }

retrieve_outputs_schema(self)

Source code in kiara/operations/included_core_operations/metadata.py

def retrieve_outputs_schema(self) -> ValueSetSchema:

    return {"value_metadata": {"type": "value_metadata", "doc": "The metadata."}}

ExtractMetadataOperationType (OperationType)

An operation that extracts metadata of a specific type from value data.

For a module profile to be picked up by this operation type, it needs to have: - exactly one input field - that input field must have the same name as its value type, or be 'value' - exactly one output field, whose field name is called 'value_metadata', and where the value has the type 'internal_model'

Source code in kiara/operations/included_core_operations/metadata.py

class ExtractMetadataOperationType(OperationType[ExtractMetadataDetails]):
    """An operation that extracts metadata of a specific type from value data.

    For a module profile to be picked up by this operation type, it needs to have:
    - exactly one input field
    - that input field must have the same name as its value type, or be 'value'
    - exactly one output field, whose field name is called 'value_metadata', and where the value has the type 'internal_model'
    """

    _operation_type_name = "extract_metadata"

    def retrieve_included_operation_configs(
        self,
    ) -> Iterable[Union[Mapping, OperationConfig]]:

        model_registry = ModelRegistry.instance()
        all_models = model_registry.get_models_of_type(ValueMetadata)

        result = []
        for model_id, model_cls_info in all_models.items():
            model_cls: Type[ValueMetadata] = model_cls_info.python_class.get_class()  # type: ignore
            metadata_key = model_cls._metadata_key  # type: ignore
            data_types = model_cls.retrieve_supported_data_types()
            if isinstance(data_types, str):
                data_types = [data_types]
            for data_type in data_types:

                config = {
                    "module_type": "value.extract_metadata",
                    "module_config": {
                        "data_type": data_type,
                        "kiara_model_id": model_cls._kiara_model_id,  # type: ignore
                    },
                    "doc": f"Extract '{metadata_key}' metadata for value type '{data_type}'.",
                }
                result.append(config)

        return result

    def check_matching_operation(
        self, module: "KiaraModule"
    ) -> Optional[ExtractMetadataDetails]:

        if len(module.outputs_schema) != 1:
            return None
        if (
            "value_metadata" not in module.outputs_schema
            or module.outputs_schema["value_metadata"].type != "internal_model"
        ):
            return None
        if len(module.inputs_schema) != 1:
            return None

        input_field_name = next(iter(module.inputs_schema.keys()))
        input_schema = module.inputs_schema.get(input_field_name)
        assert input_schema is not None
        if input_field_name != input_schema.type and input_field_name != "value":
            return None

        data_type_name = module.inputs_schema["value"].type
        model_id: str = module.get_config_value("kiara_model_id")

        registry = ModelRegistry.instance()
        metadata_model_cls = registry.get_model_cls(
            kiara_model_id=model_id, required_subclass=ValueMetadata
        )

        metadata_key = metadata_model_cls._metadata_key  # type: ignore

        if data_type_name == "any":
            op_id = f"extract.{metadata_key}.metadata"
        else:
            op_id = f"extract.{metadata_key}.metadata.from.{data_type_name}"

        details = ExtractMetadataDetails.create_operation_details(
            operation_id=op_id,
            data_type=data_type_name,
            metadata_key=metadata_key,
            input_field_name=input_field_name,
            result_field_name="value_metadata",
            is_internal_operation=True,
        )

        return details

    def get_operations_for_data_type(self, data_type: str) -> Mapping[str, Operation]:
        """Return all available metadata extract operations for the provided type (and it's parent types).

        Arguments:
            data_type: the value type

        Returns:
            a mapping with the metadata type as key, and the operation as value
        """

        lineage = set(
            self._kiara.type_registry.get_type_lineage(data_type_name=data_type)
        )

        result = {}

        for op_id, op in self.operations.items():
            op_details = self.retrieve_operation_details(op)
            included = op_details.data_type in lineage
            if not included:
                continue
            metadata_key = op_details.metadata_key
            if metadata_key in result:
                raise Exception(
                    f"Duplicate metadata operations for type '{metadata_key}'."
                )

            result[metadata_key] = op

        return result

Methods

check_matching_operation(self, module)

Check whether the provided module is a valid operation for this type.

Source code in kiara/operations/included_core_operations/metadata.py

def check_matching_operation(
    self, module: "KiaraModule"
) -> Optional[ExtractMetadataDetails]:

    if len(module.outputs_schema) != 1:
        return None
    if (
        "value_metadata" not in module.outputs_schema
        or module.outputs_schema["value_metadata"].type != "internal_model"
    ):
        return None
    if len(module.inputs_schema) != 1:
        return None

    input_field_name = next(iter(module.inputs_schema.keys()))
    input_schema = module.inputs_schema.get(input_field_name)
    assert input_schema is not None
    if input_field_name != input_schema.type and input_field_name != "value":
        return None

    data_type_name = module.inputs_schema["value"].type
    model_id: str = module.get_config_value("kiara_model_id")

    registry = ModelRegistry.instance()
    metadata_model_cls = registry.get_model_cls(
        kiara_model_id=model_id, required_subclass=ValueMetadata
    )

    metadata_key = metadata_model_cls._metadata_key  # type: ignore

    if data_type_name == "any":
        op_id = f"extract.{metadata_key}.metadata"
    else:
        op_id = f"extract.{metadata_key}.metadata.from.{data_type_name}"

    details = ExtractMetadataDetails.create_operation_details(
        operation_id=op_id,
        data_type=data_type_name,
        metadata_key=metadata_key,
        input_field_name=input_field_name,
        result_field_name="value_metadata",
        is_internal_operation=True,
    )

    return details

get_operations_for_data_type(self, data_type)

Return all available metadata extract operations for the provided type (and it's parent types).

Parameters:

Name	Type	Description	Default
data_type	str	the value type	required

Returns:

Type	Description
Mapping[str, kiara.models.module.operation.Operation]	a mapping with the metadata type as key, and the operation as value

Source code in kiara/operations/included_core_operations/metadata.py

def get_operations_for_data_type(self, data_type: str) -> Mapping[str, Operation]:
    """Return all available metadata extract operations for the provided type (and it's parent types).

    Arguments:
        data_type: the value type

    Returns:
        a mapping with the metadata type as key, and the operation as value
    """

    lineage = set(
        self._kiara.type_registry.get_type_lineage(data_type_name=data_type)
    )

    result = {}

    for op_id, op in self.operations.items():
        op_details = self.retrieve_operation_details(op)
        included = op_details.data_type in lineage
        if not included:
            continue
        metadata_key = op_details.metadata_key
        if metadata_key in result:
            raise Exception(
                f"Duplicate metadata operations for type '{metadata_key}'."
            )

        result[metadata_key] = op

    return result

retrieve_included_operation_configs(self)

Source code in kiara/operations/included_core_operations/metadata.py

def retrieve_included_operation_configs(
    self,
) -> Iterable[Union[Mapping, OperationConfig]]:

    model_registry = ModelRegistry.instance()
    all_models = model_registry.get_models_of_type(ValueMetadata)

    result = []
    for model_id, model_cls_info in all_models.items():
        model_cls: Type[ValueMetadata] = model_cls_info.python_class.get_class()  # type: ignore
        metadata_key = model_cls._metadata_key  # type: ignore
        data_types = model_cls.retrieve_supported_data_types()
        if isinstance(data_types, str):
            data_types = [data_types]
        for data_type in data_types:

            config = {
                "module_type": "value.extract_metadata",
                "module_config": {
                    "data_type": data_type,
                    "kiara_model_id": model_cls._kiara_model_id,  # type: ignore
                },
                "doc": f"Extract '{metadata_key}' metadata for value type '{data_type}'.",
            }
            result.append(config)

    return result

pipeline

logger

Classes

PipelineOperationDetails (OperationDetails) pydantic-model

Source code in kiara/operations/included_core_operations/pipeline.py

class PipelineOperationDetails(OperationDetails):
    # @classmethod
    # def create_from_module(cls, module: KiaraModule):
    #
    #     return PipelineOperationDetails(
    #         operation_id=module.module_type_name,
    #         pipeline_inputs_schema=module.inputs_schema,
    #         pipeline_outputs_schema=module.outputs_schema,
    #     )

    pipeline_inputs_schema: Mapping[str, ValueSchema] = Field(
        description="The input schema for the pipeline."
    )
    pipeline_outputs_schema: Mapping[str, ValueSchema] = Field(
        description="The output schema for the pipeline."
    )
    _op_schema: OperationSchema = PrivateAttr(default=None)

    def get_operation_schema(self) -> OperationSchema:

        if self._op_schema is not None:
            return self._op_schema

        self._op_schema = OperationSchema(
            alias=self.operation_id,
            inputs_schema=self.pipeline_inputs_schema,
            outputs_schema=self.pipeline_outputs_schema,
        )
        return self._op_schema

    def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
        return inputs

    def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:
        return outputs

Attributes

pipeline_inputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

The input schema for the pipeline.

pipeline_outputs_schema: Mapping[str, kiara.models.values.value_schema.ValueSchema] pydantic-field required

The output schema for the pipeline.

create_module_inputs(self, inputs)

Source code in kiara/operations/included_core_operations/pipeline.py

def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
    return inputs

create_operation_outputs(self, outputs)

Source code in kiara/operations/included_core_operations/pipeline.py

def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:
    return outputs

get_operation_schema(self)

Source code in kiara/operations/included_core_operations/pipeline.py

def get_operation_schema(self) -> OperationSchema:

    if self._op_schema is not None:
        return self._op_schema

    self._op_schema = OperationSchema(
        alias=self.operation_id,
        inputs_schema=self.pipeline_inputs_schema,
        outputs_schema=self.pipeline_outputs_schema,
    )
    return self._op_schema

PipelineOperationType (OperationType)

Source code in kiara/operations/included_core_operations/pipeline.py

class PipelineOperationType(OperationType[PipelineOperationDetails]):

    _operation_type_name = "pipeline"

    def __init__(self, kiara: "Kiara", op_type_name: str):

        super().__init__(kiara=kiara, op_type_name=op_type_name)
        self._pipelines = None

    @property
    def pipeline_data(self):

        if self._pipelines is not None:
            return self._pipelines

        ignore_errors = False
        pipeline_paths: Dict[
            str, Optional[Mapping[str, Any]]
        ] = find_all_kiara_pipeline_paths(skip_errors=ignore_errors)

        all_pipelines = []

        for _path in pipeline_paths.keys():
            path = Path(_path)
            if not path.exists():
                logger.warning(
                    "ignore.pipeline_path", path=path, reason="path does not exist"
                )
                continue

            elif path.is_dir():

                for root, dirnames, filenames in os.walk(path, topdown=True):

                    dirnames[:] = [d for d in dirnames if d not in DEFAULT_EXCLUDE_DIRS]

                    for filename in [
                        f
                        for f in filenames
                        if os.path.isfile(os.path.join(root, f))
                        and any(
                            f.endswith(ext) for ext in VALID_PIPELINE_FILE_EXTENSIONS
                        )
                    ]:

                        full_path = os.path.join(root, filename)
                        try:

                            data = get_pipeline_details_from_path(path=full_path)
                            data = check_doc_sidecar(full_path, data)
                            existing_metadata = data.pop("metadata", {})
                            md = dict(pipeline_paths[_path])
                            if md is None:
                                md = {}
                            md.update(existing_metadata)
                            data["metadata"] = md

                            # rel_path = os.path.relpath(os.path.dirname(full_path), path)
                            # if not rel_path or rel_path == ".":
                            #     raise NotImplementedError()
                            #     ns_name = name
                            # else:
                            #     _rel_path = rel_path.replace(os.path.sep, ".")
                            #     ns_name = f"{_rel_path}.{name}"
                            #
                            # if not ns_name:
                            #     raise Exception(
                            #         f"Could not determine namespace for pipeline file '{filename}'."
                            #     )
                            # if ns_name in files.keys():
                            #     raise Exception(
                            #         f"Duplicate workflow name: {ns_name}"
                            #     )

                            all_pipelines.append(data)

                        except Exception as e:
                            if is_debug():
                                import traceback

                                traceback.print_exc()
                            logger.warning(
                                "ignore.pipeline_file", path=full_path, reason=str(e)
                            )

            elif path.is_file():
                data = get_pipeline_details_from_path(path=path)
                data = check_doc_sidecar(path, data)
                existing_metadata = data.pop("metadata", {})
                md = dict(pipeline_paths[_path])
                if md is None:
                    md = {}
                md.update(existing_metadata)
                data["metadata"] = md
                all_pipelines.append(data)

        pipelines = {}
        for pipeline in all_pipelines:
            name = pipeline["data"].get("pipeline_name", None)
            if name is None:
                name = os.path.basename[pipeline["source"]]
                if "." in name:
                    name, _ = name.rsplit(".", maxsplit=1)
            pipelines[name] = pipeline

        return pipelines

    def retrieve_included_operation_configs(
        self,
    ) -> Iterable[Union[Mapping, OperationConfig]]:

        op_configs = []
        for pipeline_name, pipeline_data in self.pipeline_data.items():
            pipeline_config = dict(pipeline_data["data"])
            pipeline_id = pipeline_config.pop("pipeline_name", None)
            doc = pipeline_config.pop("doc", None)
            pipeline_metadata = pipeline_data["metadata"]

            op_details = PipelineOperationConfig(
                pipeline_name=pipeline_id,
                pipeline_config=pipeline_config,
                doc=doc,
                metadata=pipeline_metadata,
            )
            op_configs.append(op_details)
        return op_configs

    def check_matching_operation(
        self, module: "KiaraModule"
    ) -> Optional[PipelineOperationDetails]:

        if isinstance(module, PipelineModule):

            op_details = PipelineOperationDetails.create_operation_details(
                operation_id=module.config.pipeline_name,
                pipeline_inputs_schema=module.inputs_schema,
                pipeline_outputs_schema=module.outputs_schema,
            )
            return op_details
        else:
            return None

pipeline_data property readonly

Methods

check_matching_operation(self, module)

Check whether the provided module is a valid operation for this type.

Source code in kiara/operations/included_core_operations/pipeline.py

def check_matching_operation(
    self, module: "KiaraModule"
) -> Optional[PipelineOperationDetails]:

    if isinstance(module, PipelineModule):

        op_details = PipelineOperationDetails.create_operation_details(
            operation_id=module.config.pipeline_name,
            pipeline_inputs_schema=module.inputs_schema,
            pipeline_outputs_schema=module.outputs_schema,
        )
        return op_details
    else:
        return None

retrieve_included_operation_configs(self)

Source code in kiara/operations/included_core_operations/pipeline.py

def retrieve_included_operation_configs(
    self,
) -> Iterable[Union[Mapping, OperationConfig]]:

    op_configs = []
    for pipeline_name, pipeline_data in self.pipeline_data.items():
        pipeline_config = dict(pipeline_data["data"])
        pipeline_id = pipeline_config.pop("pipeline_name", None)
        doc = pipeline_config.pop("doc", None)
        pipeline_metadata = pipeline_data["metadata"]

        op_details = PipelineOperationConfig(
            pipeline_name=pipeline_id,
            pipeline_config=pipeline_config,
            doc=doc,
            metadata=pipeline_metadata,
        )
        op_configs.append(op_details)
    return op_configs

pretty_print

Classes

PrettyPrintDetails (BaseOperationDetails) pydantic-model

Source code in kiara/operations/included_core_operations/pretty_print.py

class PrettyPrintDetails(BaseOperationDetails):

    source_type: str = Field(description="The type of the value to be rendered.")
    target_type: str = Field(description="The type of the render result.")

    def retrieve_inputs_schema(self) -> ValueSetSchema:

        return {
            "value": {"type": "any", "doc": "The value to persist."},
            "render_type": {
                "type": "string",
                "doc": "The render target/type of render output.",
            },
            "render_config": {
                "type": "dict",
                "doc": "A value type specific configuration for how to render the data.",
                "optional": True,
            },
        }

    def retrieve_outputs_schema(self) -> ValueSetSchema:

        return {"rendered_value": {"type": "any", "doc": "The rendered value."}}

    def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:

        return {
            self.source_type: inputs["value"],
            "render_config": inputs.get("render_config", None),
        }

    def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:

        return {"rendered_value": outputs.get_value_obj("rendered_value")}

Attributes

source_type: str pydantic-field required

The type of the value to be rendered.

target_type: str pydantic-field required

The type of the render result.

create_module_inputs(self, inputs)

Source code in kiara/operations/included_core_operations/pretty_print.py

def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:

    return {
        self.source_type: inputs["value"],
        "render_config": inputs.get("render_config", None),
    }

create_operation_outputs(self, outputs)

Source code in kiara/operations/included_core_operations/pretty_print.py

def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:

    return {"rendered_value": outputs.get_value_obj("rendered_value")}

retrieve_inputs_schema(self)

Source code in kiara/operations/included_core_operations/pretty_print.py

def retrieve_inputs_schema(self) -> ValueSetSchema:

    return {
        "value": {"type": "any", "doc": "The value to persist."},
        "render_type": {
            "type": "string",
            "doc": "The render target/type of render output.",
        },
        "render_config": {
            "type": "dict",
            "doc": "A value type specific configuration for how to render the data.",
            "optional": True,
        },
    }

retrieve_outputs_schema(self)

Source code in kiara/operations/included_core_operations/pretty_print.py

def retrieve_outputs_schema(self) -> ValueSetSchema:

    return {"rendered_value": {"type": "any", "doc": "The rendered value."}}

PrettyPrintOperationType (OperationType)

An operation that takes a value, and renders into a format that can be printed for output..

For a module profile to be picked up by this operation type, it needs to have: - exactly one output field named "rendered_value" - exactly two input fields, one of them named after the type it supports, and the other called 'render_config', of type 'dict'

Source code in kiara/operations/included_core_operations/pretty_print.py

class PrettyPrintOperationType(OperationType[PrettyPrintDetails]):
    """An operation that takes a value, and renders into a format that can be printed for output..

    For a module profile to be picked up by this operation type, it needs to have:
    - exactly one output field named "rendered_value"
    - exactly two input fields, one of them named after the type it supports, and the other called 'render_config', of type 'dict'
    """

    _operation_type_name = "pretty_print"

    def _calculate_op_id(self, source_type: str, target_type: str):

        if source_type == "any":
            operation_id = f"pretty_print.as.{target_type}"
        else:
            operation_id = f"pretty_print.{source_type}.as.{target_type}"

        return operation_id

    def retrieve_included_operation_configs(
        self,
    ) -> Iterable[Union[Mapping, OperationConfig]]:

        result = {}
        for name, module_cls in self._kiara.module_type_classes.items():

            if not issubclass(module_cls, PrettyPrintModule):
                continue

            for (
                source_type,
                target_type,
            ) in module_cls.retrieve_supported_render_combinations():
                if source_type not in self._kiara.data_type_names:
                    log_message("ignore.operation_config", operation_type="pretty_print", module_type=module_cls._module_type_name, source_type=source_type, target_type=target_type, reason=f"Source type '{source_type}' not registered.")  # type: ignore
                    continue
                if target_type not in self._kiara.data_type_names:
                    log_message(
                        "ignore.operation_config",
                        operation_type="pretty_print",
                        module_type=module_cls._module_type_name,
                        source_type=source_type,  # type: ignore
                        target_type=target_type,
                        reason=f"Target type '{target_type}' not registered.",
                    )
                    continue
                func_name = f"pretty_print__{source_type}__as__{target_type}"
                attr = getattr(module_cls, func_name)
                doc = DocumentationMetadataModel.from_function(attr)
                mc = {"source_type": source_type, "target_type": target_type}
                oc = ManifestOperationConfig(
                    module_type=name, module_config=mc, doc=doc
                )
                op_id = self._calculate_op_id(
                    source_type=source_type, target_type=target_type
                )
                result[op_id] = oc

        for data_type_name, data_type_class in self._kiara.data_type_classes.items():
            for attr in dir(data_type_class):
                if not attr.startswith("pretty_print_as__"):
                    continue

                target_type = attr[17:]
                if target_type not in self._kiara.data_type_names:
                    log_message(
                        "operation_config.ignore",
                        operation_type="pretty_print",
                        source_type=data_type_name,
                        target_type=target_type,
                        reason=f"Target type '{target_type}' not registered.",
                    )  # type: ignore

                # TODO: inspect signature?
                doc = DocumentationMetadataModel.from_string(
                    f"Pretty print a {data_type_name} value as a {target_type}."
                )
                mc = {
                    "source_type": data_type_name,
                    "target_type": target_type,
                }
                oc = ManifestOperationConfig(
                    module_type="pretty_print.value", module_config=mc, doc=doc
                )
                result[f"_type_{data_type_name}"] = oc

        return result.values()

    def check_matching_operation(
        self, module: "KiaraModule"
    ) -> Optional[PrettyPrintDetails]:

        details = self.extract_details(module)

        if details is None:
            return None
        else:
            return details

    def extract_details(self, module: "KiaraModule") -> Optional[PrettyPrintDetails]:

        if len(module.inputs_schema) != 2 or len(module.outputs_schema) != 1:
            return None

        target_type = None
        for field_name, schema in module.outputs_schema.items():
            if field_name != "rendered_value":
                return None
            target_type = schema.type

        if target_type is None:
            raise Exception("No target type available.")

        input_field_match = None
        render_config_match = None

        for field_name, schema in module.inputs_schema.items():
            if field_name == schema.type:
                if input_field_match is not None:
                    # we can't deal (yet) with multiple fields
                    log_message(
                        "operation.ignore",
                        module=module.module_type_name,
                        reason=f"more than one input fields of type '{schema.type}'",
                    )
                    input_field_match = None
                    break
                else:
                    input_field_match = field_name
            elif field_name == "render_config":
                render_config_match = field_name

        if input_field_match is None:
            return None

        if render_config_match is None:
            return None

        input_field_type = module.inputs_schema[input_field_match].type

        operation_id = self._calculate_op_id(
            source_type=input_field_type, target_type=target_type
        )

        details = {
            "operation_id": operation_id,
            "source_type": input_field_type,
            "target_type": target_type,
            "is_internal_operation": True,
        }

        result = PrettyPrintDetails.create_operation_details(**details)
        return result

    def get_target_types_for(self, source_type: str) -> Mapping[str, Operation]:

        # TODO: support for sub-types
        result: Dict[str, Operation] = {}
        for operation in self.operations.values():
            details = self.retrieve_operation_details(operation)

            if details.source_type == source_type:
                target_type = details.target_type
                if target_type in result.keys():
                    raise Exception(
                        f"More than one operation for pretty_print combination '{source_type}'/'{target_type}', this is not supported (for now)."
                    )
                result[target_type] = operation

        return result

    def get_operation_for_render_combination(
        self, source_type: str, target_type: str
    ) -> Operation:

        type_lineage = self._kiara.type_registry.get_type_lineage(
            data_type_name=source_type
        )

        for st in type_lineage:
            target_types = self.get_target_types_for(source_type=st)
            if not target_types:
                continue
            if target_type not in target_types.keys():
                raise Exception(
                    f"No operation that produces '{target_type}' for source type: {st}."
                )
            result = target_types[target_type]
            return result

        raise Exception(f"No pretty_print opration(s) for source type: {source_type}.")

Methods

check_matching_operation(self, module)

Check whether the provided module is a valid operation for this type.

Source code in kiara/operations/included_core_operations/pretty_print.py

def check_matching_operation(
    self, module: "KiaraModule"
) -> Optional[PrettyPrintDetails]:

    details = self.extract_details(module)

    if details is None:
        return None
    else:
        return details

extract_details(self, module)

Source code in kiara/operations/included_core_operations/pretty_print.py

def extract_details(self, module: "KiaraModule") -> Optional[PrettyPrintDetails]:

    if len(module.inputs_schema) != 2 or len(module.outputs_schema) != 1:
        return None

    target_type = None
    for field_name, schema in module.outputs_schema.items():
        if field_name != "rendered_value":
            return None
        target_type = schema.type

    if target_type is None:
        raise Exception("No target type available.")

    input_field_match = None
    render_config_match = None

    for field_name, schema in module.inputs_schema.items():
        if field_name == schema.type:
            if input_field_match is not None:
                # we can't deal (yet) with multiple fields
                log_message(
                    "operation.ignore",
                    module=module.module_type_name,
                    reason=f"more than one input fields of type '{schema.type}'",
                )
                input_field_match = None
                break
            else:
                input_field_match = field_name
        elif field_name == "render_config":
            render_config_match = field_name

    if input_field_match is None:
        return None

    if render_config_match is None:
        return None

    input_field_type = module.inputs_schema[input_field_match].type

    operation_id = self._calculate_op_id(
        source_type=input_field_type, target_type=target_type
    )

    details = {
        "operation_id": operation_id,
        "source_type": input_field_type,
        "target_type": target_type,
        "is_internal_operation": True,
    }

    result = PrettyPrintDetails.create_operation_details(**details)
    return result

get_operation_for_render_combination(self, source_type, target_type)

Source code in kiara/operations/included_core_operations/pretty_print.py

def get_operation_for_render_combination(
    self, source_type: str, target_type: str
) -> Operation:

    type_lineage = self._kiara.type_registry.get_type_lineage(
        data_type_name=source_type
    )

    for st in type_lineage:
        target_types = self.get_target_types_for(source_type=st)
        if not target_types:
            continue
        if target_type not in target_types.keys():
            raise Exception(
                f"No operation that produces '{target_type}' for source type: {st}."
            )
        result = target_types[target_type]
        return result

    raise Exception(f"No pretty_print opration(s) for source type: {source_type}.")

get_target_types_for(self, source_type)

Source code in kiara/operations/included_core_operations/pretty_print.py

def get_target_types_for(self, source_type: str) -> Mapping[str, Operation]:

    # TODO: support for sub-types
    result: Dict[str, Operation] = {}
    for operation in self.operations.values():
        details = self.retrieve_operation_details(operation)

        if details.source_type == source_type:
            target_type = details.target_type
            if target_type in result.keys():
                raise Exception(
                    f"More than one operation for pretty_print combination '{source_type}'/'{target_type}', this is not supported (for now)."
                )
            result[target_type] = operation

    return result

retrieve_included_operation_configs(self)

Source code in kiara/operations/included_core_operations/pretty_print.py

def retrieve_included_operation_configs(
    self,
) -> Iterable[Union[Mapping, OperationConfig]]:

    result = {}
    for name, module_cls in self._kiara.module_type_classes.items():

        if not issubclass(module_cls, PrettyPrintModule):
            continue

        for (
            source_type,
            target_type,
        ) in module_cls.retrieve_supported_render_combinations():
            if source_type not in self._kiara.data_type_names:
                log_message("ignore.operation_config", operation_type="pretty_print", module_type=module_cls._module_type_name, source_type=source_type, target_type=target_type, reason=f"Source type '{source_type}' not registered.")  # type: ignore
                continue
            if target_type not in self._kiara.data_type_names:
                log_message(
                    "ignore.operation_config",
                    operation_type="pretty_print",
                    module_type=module_cls._module_type_name,
                    source_type=source_type,  # type: ignore
                    target_type=target_type,
                    reason=f"Target type '{target_type}' not registered.",
                )
                continue
            func_name = f"pretty_print__{source_type}__as__{target_type}"
            attr = getattr(module_cls, func_name)
            doc = DocumentationMetadataModel.from_function(attr)
            mc = {"source_type": source_type, "target_type": target_type}
            oc = ManifestOperationConfig(
                module_type=name, module_config=mc, doc=doc
            )
            op_id = self._calculate_op_id(
                source_type=source_type, target_type=target_type
            )
            result[op_id] = oc

    for data_type_name, data_type_class in self._kiara.data_type_classes.items():
        for attr in dir(data_type_class):
            if not attr.startswith("pretty_print_as__"):
                continue

            target_type = attr[17:]
            if target_type not in self._kiara.data_type_names:
                log_message(
                    "operation_config.ignore",
                    operation_type="pretty_print",
                    source_type=data_type_name,
                    target_type=target_type,
                    reason=f"Target type '{target_type}' not registered.",
                )  # type: ignore

            # TODO: inspect signature?
            doc = DocumentationMetadataModel.from_string(
                f"Pretty print a {data_type_name} value as a {target_type}."
            )
            mc = {
                "source_type": data_type_name,
                "target_type": target_type,
            }
            oc = ManifestOperationConfig(
                module_type="pretty_print.value", module_config=mc, doc=doc
            )
            result[f"_type_{data_type_name}"] = oc

    return result.values()

render_value

logger

Classes

RenderValueDetails (BaseOperationDetails) pydantic-model

A model that contains information needed to describe an 'extract_metadata' operation.

Source code in kiara/operations/included_core_operations/render_value.py

class RenderValueDetails(BaseOperationDetails):
    """A model that contains information needed to describe an 'extract_metadata' operation."""

    source_data_type: str = Field(description="The data type that will be rendered.")
    rendered_type: str = Field(description="The type of the render output.")
    input_field_name: str = Field(description="The input field name.")
    rendered_field_name: str = Field(description="The result field name.")

    def retrieve_inputs_schema(self) -> ValueSetSchema:
        return {
            "value": {
                "type": self.source_data_type,
                "doc": f"The {self.source_data_type} value to extract metadata from.",
            },
            "render_instruction": {
                "type": "render_instruction",
                "doc": "Configuration how to render the value.",
                "default": {},
            },
        }

    def retrieve_outputs_schema(self) -> ValueSetSchema:

        return {
            "rendered_value": {"type": "value_metadata", "doc": "The rendered data."},
            "render_metadata": {
                "type": "render_metadata",
                "doc": "Metadata associated with this render process.",
            },
        }

    def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
        return {
            self.input_field_name: inputs["value"],
            "render_instruction": inputs["render_instruction"],
        }

    def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:
        return {
            "rendered_value": outputs[self.rendered_type],
            "render_metadata": outputs["render_metadata"],
        }

Attributes

input_field_name: str pydantic-field required

The input field name.

rendered_field_name: str pydantic-field required

The result field name.

rendered_type: str pydantic-field required

The type of the render output.

source_data_type: str pydantic-field required

The data type that will be rendered.

create_module_inputs(self, inputs)

Source code in kiara/operations/included_core_operations/render_value.py

def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
    return {
        self.input_field_name: inputs["value"],
        "render_instruction": inputs["render_instruction"],
    }

create_operation_outputs(self, outputs)

Source code in kiara/operations/included_core_operations/render_value.py

def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:
    return {
        "rendered_value": outputs[self.rendered_type],
        "render_metadata": outputs["render_metadata"],
    }

retrieve_inputs_schema(self)

Source code in kiara/operations/included_core_operations/render_value.py

def retrieve_inputs_schema(self) -> ValueSetSchema:
    return {
        "value": {
            "type": self.source_data_type,
            "doc": f"The {self.source_data_type} value to extract metadata from.",
        },
        "render_instruction": {
            "type": "render_instruction",
            "doc": "Configuration how to render the value.",
            "default": {},
        },
    }

retrieve_outputs_schema(self)

Source code in kiara/operations/included_core_operations/render_value.py

def retrieve_outputs_schema(self) -> ValueSetSchema:

    return {
        "rendered_value": {"type": "value_metadata", "doc": "The rendered data."},
        "render_metadata": {
            "type": "render_metadata",
            "doc": "Metadata associated with this render process.",
        },
    }

RenderValueOperationType (OperationType)

An operation that renders a value.

Source code in kiara/operations/included_core_operations/render_value.py

class RenderValueOperationType(OperationType[RenderValueDetails]):
    """An operation that renders a value."""

    _operation_type_name = "render_value"

    def retrieve_included_operation_configs(
        self,
    ) -> Iterable[Union[Mapping, OperationConfig]]:

        model_registry = ModelRegistry.instance()
        all_models = model_registry.get_models_of_type(RenderInstruction)

        result = []
        for model_id, model_cls_info in all_models.items():
            model_cls: Type[RenderInstruction] = model_cls_info.python_class.get_class()  # type: ignore
            source_type = model_cls.retrieve_source_type()
            supported_target_types = model_cls.retrieve_supported_target_types()

            for target in supported_target_types:

                config = {
                    "module_type": "render.value",
                    "module_config": {
                        "render_instruction_type": model_id,
                        "target_type": target,
                    },
                    "doc": f"Render a '{source_type}' value as '{target}'.",
                }
                result.append(config)

        return result

    def check_matching_operation(
        self, module: "KiaraModule"
    ) -> Optional[RenderValueDetails]:

        if len(module.inputs_schema) != 2:
            return None

        if len(module.outputs_schema) != 2:
            return None

        if (
            "render_instruction" not in module.inputs_schema.keys()
            or module.inputs_schema["render_instruction"].type != "render_instruction"
        ):
            return None

        if (
            "render_metadata" not in module.outputs_schema.keys()
            or module.outputs_schema["render_metadata"].type != "render_metadata"
        ):
            return None

        source_type = None
        for field, schema in module.inputs_schema.items():
            if field == "render_instruction":
                continue
            if field == schema.type:
                if source_type:
                    log_message(
                        "operation.ignore",
                        module=module.module_type_name,
                        reason=f"more than one potential source types: {schema.type} - {source_type}",
                    )
                    return None
                source_type = field

        if not source_type:
            return None

        target_type = None
        for field, schema in module.outputs_schema.items():
            if field == "render_metadata":
                continue
            if field == schema.type:
                if target_type:
                    log_message(
                        "operation.ignore",
                        module=module.module_type_name,
                        reason=f"more than one potential target types: {schema.type} - {target_type}",
                    )
                    return None
                target_type = field

        if not target_type:
            return None

        if source_type == "any":
            op_id = f"render.value.as.{target_type}"
        else:
            op_id = f"render.{source_type}.as.{target_type}"

        details = RenderValueDetails.create_operation_details(
            operation_id=op_id,
            source_data_type=source_type,
            rendered_type=target_type,
            input_field_name=source_type,
            rendered_field_name=target_type,
            is_internal_operation=True,
        )

        return details

    def get_render_operations_for_source_type(
        self, source_type: str
    ) -> Mapping[str, Operation]:
        """Return all render operations for the specified data type.

        Arguments:
            source_type: the data type to render

        Returns:
            a mapping with the target type as key, and the operation as value
        """

        lineage = set(
            self._kiara.type_registry.get_type_lineage(data_type_name=source_type)
        )

        result: Dict[str, Operation] = {}

        for data_type in lineage:

            for op_id, op in self.operations.items():
                op_details = self.retrieve_operation_details(op)
                match = op_details.source_data_type == data_type
                if not match:
                    continue
                target_type = op_details.rendered_type
                if target_type in result.keys():
                    continue
                result[target_type] = op

        return result

    def get_render_operation(
        self, source_type: str, target_type: str
    ) -> Optional[Operation]:

        all_ops = self.get_render_operations_for_source_type(source_type=source_type)
        return all_ops.get(target_type, None)

Methods

check_matching_operation(self, module)

Check whether the provided module is a valid operation for this type.

Source code in kiara/operations/included_core_operations/render_value.py

def check_matching_operation(
    self, module: "KiaraModule"
) -> Optional[RenderValueDetails]:

    if len(module.inputs_schema) != 2:
        return None

    if len(module.outputs_schema) != 2:
        return None

    if (
        "render_instruction" not in module.inputs_schema.keys()
        or module.inputs_schema["render_instruction"].type != "render_instruction"
    ):
        return None

    if (
        "render_metadata" not in module.outputs_schema.keys()
        or module.outputs_schema["render_metadata"].type != "render_metadata"
    ):
        return None

    source_type = None
    for field, schema in module.inputs_schema.items():
        if field == "render_instruction":
            continue
        if field == schema.type:
            if source_type:
                log_message(
                    "operation.ignore",
                    module=module.module_type_name,
                    reason=f"more than one potential source types: {schema.type} - {source_type}",
                )
                return None
            source_type = field

    if not source_type:
        return None

    target_type = None
    for field, schema in module.outputs_schema.items():
        if field == "render_metadata":
            continue
        if field == schema.type:
            if target_type:
                log_message(
                    "operation.ignore",
                    module=module.module_type_name,
                    reason=f"more than one potential target types: {schema.type} - {target_type}",
                )
                return None
            target_type = field

    if not target_type:
        return None

    if source_type == "any":
        op_id = f"render.value.as.{target_type}"
    else:
        op_id = f"render.{source_type}.as.{target_type}"

    details = RenderValueDetails.create_operation_details(
        operation_id=op_id,
        source_data_type=source_type,
        rendered_type=target_type,
        input_field_name=source_type,
        rendered_field_name=target_type,
        is_internal_operation=True,
    )

    return details

get_render_operation(self, source_type, target_type)

Source code in kiara/operations/included_core_operations/render_value.py

def get_render_operation(
    self, source_type: str, target_type: str
) -> Optional[Operation]:

    all_ops = self.get_render_operations_for_source_type(source_type=source_type)
    return all_ops.get(target_type, None)

get_render_operations_for_source_type(self, source_type)

Return all render operations for the specified data type.

Parameters:

Name	Type	Description	Default
source_type	str	the data type to render	required

Returns:

Type	Description
Mapping[str, kiara.models.module.operation.Operation]	a mapping with the target type as key, and the operation as value

Source code in kiara/operations/included_core_operations/render_value.py

def get_render_operations_for_source_type(
    self, source_type: str
) -> Mapping[str, Operation]:
    """Return all render operations for the specified data type.

    Arguments:
        source_type: the data type to render

    Returns:
        a mapping with the target type as key, and the operation as value
    """

    lineage = set(
        self._kiara.type_registry.get_type_lineage(data_type_name=source_type)
    )

    result: Dict[str, Operation] = {}

    for data_type in lineage:

        for op_id, op in self.operations.items():
            op_details = self.retrieve_operation_details(op)
            match = op_details.source_data_type == data_type
            if not match:
                continue
            target_type = op_details.rendered_type
            if target_type in result.keys():
                continue
            result[target_type] = op

    return result

retrieve_included_operation_configs(self)

Source code in kiara/operations/included_core_operations/render_value.py

def retrieve_included_operation_configs(
    self,
) -> Iterable[Union[Mapping, OperationConfig]]:

    model_registry = ModelRegistry.instance()
    all_models = model_registry.get_models_of_type(RenderInstruction)

    result = []
    for model_id, model_cls_info in all_models.items():
        model_cls: Type[RenderInstruction] = model_cls_info.python_class.get_class()  # type: ignore
        source_type = model_cls.retrieve_source_type()
        supported_target_types = model_cls.retrieve_supported_target_types()

        for target in supported_target_types:

            config = {
                "module_type": "render.value",
                "module_config": {
                    "render_instruction_type": model_id,
                    "target_type": target,
                },
                "doc": f"Render a '{source_type}' value as '{target}'.",
            }
            result.append(config)

    return result

serialize

Classes

DeSerializeDetails (BaseOperationDetails) pydantic-model

Source code in kiara/operations/included_core_operations/serialize.py

class DeSerializeDetails(BaseOperationDetails):

    value_type: str = Field(
        "The name of the input field for the serialized version of the value."
    )
    value_input_field: str = Field(
        "The name of the input field for the serialized version of the value."
    )
    object_output_field: str = Field(
        description="The (output) field name containing the deserialized python class."
    )
    serialization_profile: str = Field(
        description="The name for the serialization profile used on the source value."
    )
    target_profile: str = Field(description="The target profile name.")
    # target_class: PythonClass = Field(
    #     description="The python class of the result object."
    # )

    def retrieve_inputs_schema(self) -> ValueSetSchema:

        return {"value": {"type": self.value_type, "doc": "The value to de-serialize."}}

    def retrieve_outputs_schema(self) -> ValueSetSchema:

        return {
            "python_object": {
                "type": "python_object",
                "doc": "The de-serialized python object instance.",
            }
        }

    def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:

        result = {self.value_input_field: inputs["value"]}
        return result

    def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:

        return outputs

Attributes

object_output_field: str pydantic-field required

The (output) field name containing the deserialized python class.

serialization_profile: str pydantic-field required

The name for the serialization profile used on the source value.

target_profile: str pydantic-field required

The target profile name.

value_input_field: str pydantic-field

value_type: str pydantic-field

create_module_inputs(self, inputs)

Source code in kiara/operations/included_core_operations/serialize.py

def create_module_inputs(self, inputs: Mapping[str, Any]) -> Mapping[str, Any]:

    result = {self.value_input_field: inputs["value"]}
    return result

create_operation_outputs(self, outputs)

Source code in kiara/operations/included_core_operations/serialize.py

def create_operation_outputs(self, outputs: ValueMap) -> Mapping[str, Value]:

    return outputs

retrieve_inputs_schema(self)

Source code in kiara/operations/included_core_operations/serialize.py

def retrieve_inputs_schema(self) -> ValueSetSchema:

    return {"value": {"type": self.value_type, "doc": "The value to de-serialize."}}

retrieve_outputs_schema(self)

Source code in kiara/operations/included_core_operations/serialize.py

def retrieve_outputs_schema(self) -> ValueSetSchema:

    return {
        "python_object": {
            "type": "python_object",
            "doc": "The de-serialized python object instance.",
        }
    }

DeSerializeOperationType (OperationType)

An operation that takes a value, and serializes it into the format suitable to the [serialized_value][kiara.data_types.included_core_types.SeriailzedValue] value type.

For a module profile to be picked up by this operation type, it needs to have: - exactly one output field of type serialized_value - either one of (in this order): - exactly one input field - one input field where the field name equals the type name - an input field called 'value'

Source code in kiara/operations/included_core_operations/serialize.py

class DeSerializeOperationType(OperationType[DeSerializeDetails]):
    """An operation that takes a value, and serializes it into the format suitable to the [`serialized_value`][kiara.data_types.included_core_types.SeriailzedValue] value type.

    For a module profile to be picked up by this operation type, it needs to have:
    - exactly one output field of type `serialized_value`
    - either one of (in this order):
      - exactly one input field
      - one input field where the field name equals the type name
      - an input field called 'value'
    """

    _operation_type_name = "deserialize"

    def retrieve_included_operation_configs(
        self,
    ) -> Iterable[Union[Mapping, OperationConfig]]:
        result = []
        for name, module_cls in self._kiara.module_type_classes.items():

            if not hasattr(module_cls, "retrieve_serialized_value_type"):
                continue
            if not hasattr(module_cls, "retrieve_supported_target_profiles"):
                continue
            if not hasattr(module_cls, "retrieve_supported_serialization_profile"):
                continue

            try:
                value_type = module_cls.retrieve_serialized_value_type()  # type: ignore
            except TypeError:
                raise Exception(
                    f"Can't retrieve source value type for deserialization module '{module_cls.__name__}'. This is most likely a bug, maybe you are missing a '@classmethod' annotation on the 'retrieve_source_value_type' method?"
                )
            try:
                serialization_profile = module_cls.retrieve_supported_serialization_profile()  # type: ignore
            except TypeError:
                raise Exception(
                    f"Can't retrieve supported serialization profiles for deserialization module '{module_cls.__name__}'. This is most likely a bug, maybe you are missing a '@classmethod' annotation on the 'retrieve_supported_serialization_profile' method?"
                )

            try:
                target_profiles = module_cls.retrieve_supported_target_profiles()  # type: ignore
            except TypeError:
                raise Exception(
                    f"Can't retrieve supported target profile for deserialization module '{module_cls.__name__}'. This is most likely a bug, maybe you are missing a '@classmethod' annotation on the 'retrieve_supported_target_profile' method?"
                )

            for _profile_name, cls in target_profiles.items():
                func_name = f"to__{_profile_name}"
                attr = getattr(module_cls, func_name)
                doc = DocumentationMetadataModel.from_function(attr)
                mc = {
                    "value_type": value_type,
                    "target_profile": _profile_name,
                    "serialization_profile": serialization_profile
                    # "target_class": PythonClass.from_class(cls),
                }
                oc = ManifestOperationConfig(
                    module_type=name, module_config=mc, doc=doc
                )
                result.append(oc)

        return result

    def check_matching_operation(
        self, module: "KiaraModule"
    ) -> Optional[DeSerializeDetails]:

        details = self.extract_details(module)

        if details is None:
            return None
        else:
            return details

    def extract_details(self, module: "KiaraModule") -> Optional[DeSerializeDetails]:

        result_field_name = None
        for field_name, schema in module.outputs_schema.items():
            if schema.type != "python_object":
                continue
            else:
                if result_field_name is not None:
                    log_message(
                        "ignore.operation",
                        reason=f"found more than one potential result value field: {result_field_name} -- {field_name}'",
                        module_type=module.module_type_name,
                    )
                    continue
                else:
                    result_field_name = field_name

        if not result_field_name:
            return None

        input_field_name = None
        for field_name, schema in module.inputs_schema.items():
            if field_name != schema.type:
                continue
            if input_field_name is not None:
                log_message(
                    "ignore.operation",
                    reason=f"found more than one potential result value field: {result_field_name} -- {field_name}'",
                    module_type=module.module_type_name,
                )
                continue
            else:
                input_field_name = field_name

        if not input_field_name:
            return None

        try:
            value_type = module.config.get("value_type")
            target_profile = module.config.get("target_profile")
            serialization_profile = module.config.get("serialization_profile")
            # target_class = module.config.get("target_class")
        except Exception as e:
            log_message(
                "ignore.operation",
                reason=str(e),
                module_type=module.module_type_name,
            )
            return None

        if value_type not in self._kiara.type_registry.data_type_names:
            log_message(
                "ignore.operation",
                reason=f"Invalid value type: {value_type}",
                module_type=module.module_type_name,
            )
            return None

        if input_field_name == "any":
            operation_id = "deserialize.value"
        else:
            operation_id = f"deserialize.{input_field_name}.as.{target_profile}"

        details: Dict[str, Any] = {
            "operation_id": operation_id,
            "value_type": input_field_name,
            "value_input_field": input_field_name,
            "object_output_field": result_field_name,
            "target_profile": target_profile,
            "serialization_profile": serialization_profile,
            # "target_class": target_class,
            "is_internal_operation": True,
        }

        result = DeSerializeDetails.construct(**details)
        return result

    def find_deserialization_operations_for_type(
        self, type_name: str
    ) -> List[Operation]:

        lineage = self._kiara.type_registry.get_type_lineage(type_name)
        result = []
        for data_type in lineage:
            match = []
            for op in self.operations.values():
                details = self.retrieve_operation_details(op)
                if details.value_type == data_type:
                    match.append(op)

            result.extend(match)

        return result

    def find_deserialzation_operation_for_type_and_profile(
        self, type_name: str, serialization_profile: str
    ) -> List[Operation]:

        lineage = self._kiara.type_registry.get_type_lineage(type_name)
        serialize_ops: List[Operation] = []
        for data_type in lineage:
            match = []
            op = None
            for op in self.operations.values():
                details = self.retrieve_operation_details(op)
                if (
                    details.value_type == data_type
                    and details.serialization_profile == serialization_profile
                ):
                    match.append(op)

            if match:
                if len(match) > 1:
                    assert op is not None
                    raise Exception(
                        f"Multiple deserialization operations found for data type '{type_name}' and serialization profile '{serialization_profile}'. This is not supported (yet)."
                    )
                serialize_ops.append(match[0])

        return serialize_ops

Methods

check_matching_operation(self, module)

Check whether the provided module is a valid operation for this type.

Source code in kiara/operations/included_core_operations/serialize.py

def check_matching_operation(
    self, module: "KiaraModule"
) -> Optional[DeSerializeDetails]:

    details = self.extract_details(module)

    if details is None:
        return None
    else:
        return details

extract_details(self, module)

Source code in kiara/operations/included_core_operations/serialize.py

def extract_details(self, module: "KiaraModule") -> Optional[DeSerializeDetails]:

    result_field_name = None
    for field_name, schema in module.outputs_schema.items():
        if schema.type != "python_object":
            continue
        else:
            if result_field_name is not None:
                log_message(
                    "ignore.operation",
                    reason=f"found more than one potential result value field: {result_field_name} -- {field_name}'",
                    module_type=module.module_type_name,
                )
                continue
            else:
                result_field_name = field_name

    if not result_field_name:
        return None

    input_field_name = None
    for field_name, schema in module.inputs_schema.items():
        if field_name != schema.type:
            continue
        if input_field_name is not None:
            log_message(
                "ignore.operation",
                reason=f"found more than one potential result value field: {result_field_name} -- {field_name}'",
                module_type=module.module_type_name,
            )
            continue
        else:
            input_field_name = field_name

    if not input_field_name:
        return None

    try:
        value_type = module.config.get("value_type")
        target_profile = module.config.get("target_profile")
        serialization_profile = module.config.get("serialization_profile")
        # target_class = module.config.get("target_class")
    except Exception as e:
        log_message(
            "ignore.operation",
            reason=str(e),
            module_type=module.module_type_name,
        )
        return None

    if value_type not in self._kiara.type_registry.data_type_names:
        log_message(
            "ignore.operation",
            reason=f"Invalid value type: {value_type}",
            module_type=module.module_type_name,
        )
        return None

    if input_field_name == "any":
        operation_id = "deserialize.value"
    else:
        operation_id = f"deserialize.{input_field_name}.as.{target_profile}"

    details: Dict[str, Any] = {
        "operation_id": operation_id,
        "value_type": input_field_name,
        "value_input_field": input_field_name,
        "object_output_field": result_field_name,
        "target_profile": target_profile,
        "serialization_profile": serialization_profile,
        # "target_class": target_class,
        "is_internal_operation": True,
    }

    result = DeSerializeDetails.construct(**details)
    return result

find_deserialization_operations_for_type(self, type_name)

Source code in kiara/operations/included_core_operations/serialize.py

def find_deserialization_operations_for_type(
    self, type_name: str
) -> List[Operation]:

    lineage = self._kiara.type_registry.get_type_lineage(type_name)
    result = []
    for data_type in lineage:
        match = []
        for op in self.operations.values():
            details = self.retrieve_operation_details(op)
            if details.value_type == data_type:
                match.append(op)

        result.extend(match)

    return result

find_deserialzation_operation_for_type_and_profile(self, type_name, serialization_profile)

Source code in kiara/operations/included_core_operations/serialize.py

def find_deserialzation_operation_for_type_and_profile(
    self, type_name: str, serialization_profile: str
) -> List[Operation]:

    lineage = self._kiara.type_registry.get_type_lineage(type_name)
    serialize_ops: List[Operation] = []
    for data_type in lineage:
        match = []
        op = None
        for op in self.operations.values():
            details = self.retrieve_operation_details(op)
            if (
                details.value_type == data_type
                and details.serialization_profile == serialization_profile
            ):
                match.append(op)

        if match:
            if len(match) > 1:
                assert op is not None
                raise Exception(
                    f"Multiple deserialization operations found for data type '{type_name}' and serialization profile '{serialization_profile}'. This is not supported (yet)."
                )
            serialize_ops.append(match[0])

    return serialize_ops

retrieve_included_operation_configs(self)

Source code in kiara/operations/included_core_operations/serialize.py

def retrieve_included_operation_configs(
    self,
) -> Iterable[Union[Mapping, OperationConfig]]:
    result = []
    for name, module_cls in self._kiara.module_type_classes.items():

        if not hasattr(module_cls, "retrieve_serialized_value_type"):
            continue
        if not hasattr(module_cls, "retrieve_supported_target_profiles"):
            continue
        if not hasattr(module_cls, "retrieve_supported_serialization_profile"):
            continue

        try:
            value_type = module_cls.retrieve_serialized_value_type()  # type: ignore
        except TypeError:
            raise Exception(
                f"Can't retrieve source value type for deserialization module '{module_cls.__name__}'. This is most likely a bug, maybe you are missing a '@classmethod' annotation on the 'retrieve_source_value_type' method?"
            )
        try:
            serialization_profile = module_cls.retrieve_supported_serialization_profile()  # type: ignore
        except TypeError:
            raise Exception(
                f"Can't retrieve supported serialization profiles for deserialization module '{module_cls.__name__}'. This is most likely a bug, maybe you are missing a '@classmethod' annotation on the 'retrieve_supported_serialization_profile' method?"
            )

        try:
            target_profiles = module_cls.retrieve_supported_target_profiles()  # type: ignore
        except TypeError:
            raise Exception(
                f"Can't retrieve supported target profile for deserialization module '{module_cls.__name__}'. This is most likely a bug, maybe you are missing a '@classmethod' annotation on the 'retrieve_supported_target_profile' method?"
            )

        for _profile_name, cls in target_profiles.items():
            func_name = f"to__{_profile_name}"
            attr = getattr(module_cls, func_name)
            doc = DocumentationMetadataModel.from_function(attr)
            mc = {
                "value_type": value_type,
                "target_profile": _profile_name,
                "serialization_profile": serialization_profile
                # "target_class": PythonClass.from_class(cls),
            }
            oc = ManifestOperationConfig(
                module_type=name, module_config=mc, doc=doc
            )
            result.append(oc)

    return result

processing special

Classes

JobStatusListener (Protocol)

Source code in kiara/processing/__init__.py

class JobStatusListener(Protocol):
    def job_status_changed(
        self, job_id: uuid.UUID, old_status: Optional[JobStatus], new_status: JobStatus
    ):
        pass

job_status_changed(self, job_id, old_status, new_status)

Source code in kiara/processing/__init__.py

def job_status_changed(
    self, job_id: uuid.UUID, old_status: Optional[JobStatus], new_status: JobStatus
):
    pass

ModuleProcessor (ABC)

Source code in kiara/processing/__init__.py

class ModuleProcessor(abc.ABC):
    def __init__(self, kiara: "Kiara"):

        self._kiara: Kiara = kiara
        self._created_jobs: Dict[uuid.UUID, Dict[str, Any]] = {}
        self._active_jobs: Dict[uuid.UUID, ActiveJob] = {}
        self._failed_jobs: Dict[uuid.UUID, ActiveJob] = {}
        self._finished_jobs: Dict[uuid.UUID, ActiveJob] = {}
        self._output_refs: Dict[uuid.UUID, ValueMapWritable] = {}
        self._job_records: Dict[uuid.UUID, JobRecord] = {}

        self._listeners: List[JobStatusListener] = []

    def _send_job_event(
        self, job_id: uuid.UUID, old_status: Optional[JobStatus], new_status: JobStatus
    ):

        for listener in self._listeners:
            listener.job_status_changed(
                job_id=job_id, old_status=old_status, new_status=new_status
            )

    def register_job_status_listener(self, listener: JobStatusListener):

        self._listeners.append(listener)

    def get_job(self, job_id: uuid.UUID) -> ActiveJob:

        if job_id in self._active_jobs.keys():
            return self._active_jobs[job_id]
        elif job_id in self._finished_jobs.keys():
            return self._finished_jobs[job_id]
        elif job_id in self._failed_jobs.keys():
            return self._failed_jobs[job_id]
        else:
            raise Exception(f"No job with id '{job_id}' registered.")

    def get_job_status(self, job_id: uuid.UUID) -> JobStatus:

        job = self.get_job(job_id=job_id)
        return job.status

    def get_job_record(self, job_id: uuid.UUID) -> JobRecord:

        if job_id in self._job_records.keys():
            return self._job_records[job_id]
        else:
            raise Exception(f"No job record for job with id '{job_id}' registered.")

    def create_job(self, job_config: JobConfig) -> uuid.UUID:

        environments = {
            env_name: env.instance_id
            for env_name, env in self._kiara.current_environments.items()
        }

        result_pedigree = ValuePedigree(
            kiara_id=self._kiara.id,
            module_type=job_config.module_type,
            module_config=job_config.module_config,
            inputs=job_config.inputs,
            environments=environments,
        )

        module = self._kiara.create_module(manifest=job_config)

        outputs = ValueMapWritable.create_from_schema(
            kiara=self._kiara, schema=module.outputs_schema, pedigree=result_pedigree
        )
        job_id = ID_REGISTRY.generate(kiara_id=self._kiara.id)
        job_log = JobLog()

        job = ActiveJob.construct(job_id=job_id, job_config=job_config, job_log=job_log)
        ID_REGISTRY.update_metadata(job_id, obj=job)
        job.job_log.add_log("job created")

        job_details = {
            "job_config": job_config,
            "job": job,
            "module": module,
            "outputs": outputs,
        }
        self._created_jobs[job_id] = job_details

        self._send_job_event(
            job_id=job_id, old_status=None, new_status=JobStatus.CREATED
        )

        return job_id

    def queue_job(self, job_id: uuid.UUID) -> ActiveJob:

        job_details = self._created_jobs.pop(job_id)
        job_config = job_details.pop("job_config")

        job = job_details.pop("job")
        module = job_details.pop("module")
        outputs = job_details.pop("outputs")

        self._active_jobs[job_id] = job
        self._output_refs[job_id] = outputs

        input_values = self._kiara.data_registry.load_values(job_config.inputs)

        if module.is_pipeline():
            module._set_job_registry(self._kiara.job_registry)

        try:
            self._add_processing_task(
                job_id=job_id,
                module=module,
                inputs=input_values,
                outputs=outputs,
                job_log=job.job_log,
            )
            return job

        except Exception as e:
            msg = str(e)
            if not msg:
                msg = repr(e)
            job.error = msg
            if is_debug():
                try:
                    import traceback

                    traceback.print_exc()
                except Exception:
                    pass
            if isinstance(e, KiaraProcessingException):
                e._module = module
                e._inputs = ValueMapReadOnly.create_from_ids(
                    data_registry=self._kiara.data_registry, **job_config.inputs
                )
                job._exception = e
                raise e
            else:
                kpe = KiaraProcessingException(
                    e,
                    module=module,
                    inputs=ValueMapReadOnly.create_from_ids(
                        self._kiara.data_registry, **job_config.inputs
                    ),
                )
                job._exception = kpe
                raise kpe

    def job_status_updated(
        self, job_id: uuid.UUID, status: Union[JobStatus, str, Exception]
    ):

        job = self._active_jobs.get(job_id, None)
        if job is None:
            raise Exception(
                f"Can't retrieve active job with id '{job_id}', no such job registered."
            )

        old_status = job.status

        if status == JobStatus.SUCCESS:
            self._active_jobs.pop(job_id)
            job.job_log.add_log("job finished successfully")
            job.status = JobStatus.SUCCESS
            job.finished = datetime.now()
            values = self._output_refs[job_id]
            values.sync_values()
            value_ids = values.get_all_value_ids()
            job.results = value_ids
            job.job_log.percent_finished = 100
            job_record = JobRecord.from_active_job(active_job=job)
            self._job_records[job_id] = job_record
            self._finished_jobs[job_id] = job
        elif status == JobStatus.FAILED or isinstance(status, (str, Exception)):
            self._active_jobs.pop(job_id)
            job.job_log.add_log("job failed")
            job.status = JobStatus.FAILED
            job.finished = datetime.now()
            if isinstance(status, str):
                job.error = status
            elif isinstance(status, Exception):
                msg = str(status)
                job.error = msg
                job._exception = status
            self._failed_jobs[job_id] = job
        elif status == JobStatus.STARTED:
            job.job_log.add_log("job started")
            job.status = JobStatus.STARTED
            job.started = datetime.now()
        else:
            raise ValueError(f"Invalid value for status: {status}")

        self._send_job_event(
            job_id=job_id, old_status=old_status, new_status=job.status
        )

    def wait_for(self, *job_ids: uuid.UUID):
        """Wait for the jobs with the specified ids, also optionally sync their outputs with the pipeline value state."""

        self._wait_for(*job_ids)

        for job_id in job_ids:
            job = self._job_records.get(job_id, None)
            if job is None:
                _job = self._failed_jobs.get(job_id, None)
                if _job is None:
                    raise Exception(f"Can't find job with id: {job_id}")

    @abc.abstractmethod
    def _wait_for(self, *job_ids: uuid.UUID):
        pass

    @abc.abstractmethod
    def _add_processing_task(
        self,
        job_id: uuid.UUID,
        module: "KiaraModule",
        inputs: ValueMap,
        outputs: ValueMapWritable,
        job_log: JobLog,
    ) -> str:
        pass

Methods

create_job(self, job_config)

Source code in kiara/processing/__init__.py

def create_job(self, job_config: JobConfig) -> uuid.UUID:

    environments = {
        env_name: env.instance_id
        for env_name, env in self._kiara.current_environments.items()
    }

    result_pedigree = ValuePedigree(
        kiara_id=self._kiara.id,
        module_type=job_config.module_type,
        module_config=job_config.module_config,
        inputs=job_config.inputs,
        environments=environments,
    )

    module = self._kiara.create_module(manifest=job_config)

    outputs = ValueMapWritable.create_from_schema(
        kiara=self._kiara, schema=module.outputs_schema, pedigree=result_pedigree
    )
    job_id = ID_REGISTRY.generate(kiara_id=self._kiara.id)
    job_log = JobLog()

    job = ActiveJob.construct(job_id=job_id, job_config=job_config, job_log=job_log)
    ID_REGISTRY.update_metadata(job_id, obj=job)
    job.job_log.add_log("job created")

    job_details = {
        "job_config": job_config,
        "job": job,
        "module": module,
        "outputs": outputs,
    }
    self._created_jobs[job_id] = job_details

    self._send_job_event(
        job_id=job_id, old_status=None, new_status=JobStatus.CREATED
    )

    return job_id

get_job(self, job_id)

Source code in kiara/processing/__init__.py

def get_job(self, job_id: uuid.UUID) -> ActiveJob:

    if job_id in self._active_jobs.keys():
        return self._active_jobs[job_id]
    elif job_id in self._finished_jobs.keys():
        return self._finished_jobs[job_id]
    elif job_id in self._failed_jobs.keys():
        return self._failed_jobs[job_id]
    else:
        raise Exception(f"No job with id '{job_id}' registered.")

get_job_record(self, job_id)

Source code in kiara/processing/__init__.py

def get_job_record(self, job_id: uuid.UUID) -> JobRecord:

    if job_id in self._job_records.keys():
        return self._job_records[job_id]
    else:
        raise Exception(f"No job record for job with id '{job_id}' registered.")

get_job_status(self, job_id)

Source code in kiara/processing/__init__.py

def get_job_status(self, job_id: uuid.UUID) -> JobStatus:

    job = self.get_job(job_id=job_id)
    return job.status

job_status_updated(self, job_id, status)

Source code in kiara/processing/__init__.py

def job_status_updated(
    self, job_id: uuid.UUID, status: Union[JobStatus, str, Exception]
):

    job = self._active_jobs.get(job_id, None)
    if job is None:
        raise Exception(
            f"Can't retrieve active job with id '{job_id}', no such job registered."
        )

    old_status = job.status

    if status == JobStatus.SUCCESS:
        self._active_jobs.pop(job_id)
        job.job_log.add_log("job finished successfully")
        job.status = JobStatus.SUCCESS
        job.finished = datetime.now()
        values = self._output_refs[job_id]
        values.sync_values()
        value_ids = values.get_all_value_ids()
        job.results = value_ids
        job.job_log.percent_finished = 100
        job_record = JobRecord.from_active_job(active_job=job)
        self._job_records[job_id] = job_record
        self._finished_jobs[job_id] = job
    elif status == JobStatus.FAILED or isinstance(status, (str, Exception)):
        self._active_jobs.pop(job_id)
        job.job_log.add_log("job failed")
        job.status = JobStatus.FAILED
        job.finished = datetime.now()
        if isinstance(status, str):
            job.error = status
        elif isinstance(status, Exception):
            msg = str(status)
            job.error = msg
            job._exception = status
        self._failed_jobs[job_id] = job
    elif status == JobStatus.STARTED:
        job.job_log.add_log("job started")
        job.status = JobStatus.STARTED
        job.started = datetime.now()
    else:
        raise ValueError(f"Invalid value for status: {status}")

    self._send_job_event(
        job_id=job_id, old_status=old_status, new_status=job.status
    )

queue_job(self, job_id)

Source code in kiara/processing/__init__.py

def queue_job(self, job_id: uuid.UUID) -> ActiveJob:

    job_details = self._created_jobs.pop(job_id)
    job_config = job_details.pop("job_config")

    job = job_details.pop("job")
    module = job_details.pop("module")
    outputs = job_details.pop("outputs")

    self._active_jobs[job_id] = job
    self._output_refs[job_id] = outputs

    input_values = self._kiara.data_registry.load_values(job_config.inputs)

    if module.is_pipeline():
        module._set_job_registry(self._kiara.job_registry)

    try:
        self._add_processing_task(
            job_id=job_id,
            module=module,
            inputs=input_values,
            outputs=outputs,
            job_log=job.job_log,
        )
        return job

    except Exception as e:
        msg = str(e)
        if not msg:
            msg = repr(e)
        job.error = msg
        if is_debug():
            try:
                import traceback

                traceback.print_exc()
            except Exception:
                pass
        if isinstance(e, KiaraProcessingException):
            e._module = module
            e._inputs = ValueMapReadOnly.create_from_ids(
                data_registry=self._kiara.data_registry, **job_config.inputs
            )
            job._exception = e
            raise e
        else:
            kpe = KiaraProcessingException(
                e,
                module=module,
                inputs=ValueMapReadOnly.create_from_ids(
                    self._kiara.data_registry, **job_config.inputs
                ),
            )
            job._exception = kpe
            raise kpe

register_job_status_listener(self, listener)

Source code in kiara/processing/__init__.py

def register_job_status_listener(self, listener: JobStatusListener):

    self._listeners.append(listener)

wait_for(self, *job_ids)

Wait for the jobs with the specified ids, also optionally sync their outputs with the pipeline value state.

Source code in kiara/processing/__init__.py

def wait_for(self, *job_ids: uuid.UUID):
    """Wait for the jobs with the specified ids, also optionally sync their outputs with the pipeline value state."""

    self._wait_for(*job_ids)

    for job_id in job_ids:
        job = self._job_records.get(job_id, None)
        if job is None:
            _job = self._failed_jobs.get(job_id, None)
            if _job is None:
                raise Exception(f"Can't find job with id: {job_id}")

ProcessorConfig (BaseModel) pydantic-model

Source code in kiara/processing/__init__.py

class ProcessorConfig(BaseModel):

    module_processor_type: Literal["synchronous", "multi-threaded"] = "synchronous"

module_processor_type: Literal['synchronous', 'multi-threaded'] pydantic-field

synchronous

SynchronousProcessor (ModuleProcessor)

Source code in kiara/processing/synchronous.py

class SynchronousProcessor(ModuleProcessor):
    def _add_processing_task(
        self,
        job_id: uuid.UUID,
        module: "KiaraModule",
        inputs: ValueMap,
        outputs: ValueMapWritable,
        job_log: JobLog,
    ):

        self.job_status_updated(job_id=job_id, status=JobStatus.STARTED)
        try:
            module.process_step(inputs=inputs, outputs=outputs, job_log=job_log)
            # output_wrap._sync()
            self.job_status_updated(job_id=job_id, status=JobStatus.SUCCESS)
        except Exception as e:
            self.job_status_updated(job_id=job_id, status=e)

    def _wait_for(self, *job_ids: uuid.UUID):

        # jobs will always be finished, since we were waiting for them in the 'process' method
        return

SynchronousProcessorConfig (ProcessorConfig) pydantic-model

Source code in kiara/processing/synchronous.py

class SynchronousProcessorConfig(ProcessorConfig):

    pass

registries special

ARCHIVE_CONFIG_CLS

Classes

ArchiveConfig (BaseModel) pydantic-model

Source code in kiara/registries/__init__.py

class ArchiveConfig(BaseModel):
    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps

Config

Source code in kiara/registries/__init__.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/registries/__init__.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

BaseArchive (KiaraArchive, Generic)

Source code in kiara/registries/__init__.py

class BaseArchive(KiaraArchive, Generic[ARCHIVE_CONFIG_CLS]):

    _config_cls = ArchiveConfig

    def __init__(self, archive_id: uuid.UUID, config: ARCHIVE_CONFIG_CLS):

        self._archive_id: uuid.UUID = archive_id
        self._config: ARCHIVE_CONFIG_CLS = config
        self._kiara: Optional["Kiara"] = None

    @property
    def config(self) -> ARCHIVE_CONFIG_CLS:

        return self._config

    @property
    def archive_id(self) -> uuid.UUID:
        return self._archive_id

    @property
    def kiara_context(self) -> "Kiara":
        if self._kiara is None:
            raise Exception("Archive not registered into a kiara context yet.")
        return self._kiara

    def register_archive(self, kiara: "Kiara") -> uuid.UUID:
        self._kiara = kiara
        return self.archive_id

archive_id: UUID property readonly

config: ~ARCHIVE_CONFIG_CLS property readonly

kiara_context: Kiara property readonly

_config_cls (BaseModel) private pydantic-model

Source code in kiara/registries/__init__.py

class ArchiveConfig(BaseModel):
    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps

Config

Source code in kiara/registries/__init__.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/registries/__init__.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

register_archive(self, kiara)

Source code in kiara/registries/__init__.py

def register_archive(self, kiara: "Kiara") -> uuid.UUID:
    self._kiara = kiara
    return self.archive_id

FileSystemArchiveConfig (ArchiveConfig) pydantic-model

Source code in kiara/registries/__init__.py

class FileSystemArchiveConfig(ArchiveConfig):

    archive_path: str = Field(
        description="The path where the data for this archive is stored."
    )

Attributes

archive_path: str pydantic-field required

The path where the data for this archive is stored.

KiaraArchive (ABC)

Source code in kiara/registries/__init__.py

class KiaraArchive(abc.ABC):

    _config_cls: ClassVar[Type[ArchiveConfig]] = ArchiveConfig

    @classmethod
    @abc.abstractmethod
    def supported_item_types(cls) -> Iterable[str]:
        pass

    @classmethod
    @abc.abstractmethod
    def is_writeable(cls) -> bool:
        pass

    @abc.abstractmethod
    def register_archive(self, kiara: "Kiara") -> uuid.UUID:
        pass

_config_cls (BaseModel) private pydantic-model

Source code in kiara/registries/__init__.py

class ArchiveConfig(BaseModel):
    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps

Config

Source code in kiara/registries/__init__.py

class Config:
    json_loads = orjson.loads
    json_dumps = orjson_dumps

json_loads

json_dumps(v, *, default=None, **args)

Source code in kiara/registries/__init__.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

is_writeable() classmethod

Source code in kiara/registries/__init__.py

@classmethod
@abc.abstractmethod
def is_writeable(cls) -> bool:
    pass

register_archive(self, kiara)

Source code in kiara/registries/__init__.py

@abc.abstractmethod
def register_archive(self, kiara: "Kiara") -> uuid.UUID:
    pass

supported_item_types() classmethod

Source code in kiara/registries/__init__.py

@classmethod
@abc.abstractmethod
def supported_item_types(cls) -> Iterable[str]:
    pass

Modules

aliases special

logger

Classes

AliasArchive (BaseArchive)

Source code in kiara/registries/aliases/__init__.py

class AliasArchive(BaseArchive):
    @classmethod
    def supported_item_types(cls) -> Iterable[str]:
        return ["alias"]

    @abc.abstractmethod
    def retrieve_all_aliases(self) -> Optional[Mapping[str, uuid.UUID]]:
        """Retrieve a list of all aliases registered in this archive.

        The result of this method can be 'None', for cases where the aliases are determined dynamically.
        In kiara, the result of this method is mostly used to improve performance when looking up an alias.

        Returns:
            a list of strings (the aliases), or 'None' if this archive does not support alias indexes.
        """

    @abc.abstractmethod
    def find_value_id_for_alias(self, alias: str) -> Optional[uuid.UUID]:
        pass

    @abc.abstractmethod
    def find_aliases_for_value_id(self, value_id: uuid.UUID) -> Optional[Set[str]]:
        pass

    @classmethod
    def is_writeable(cls) -> bool:
        return False

Methods

find_aliases_for_value_id(self, value_id)

Source code in kiara/registries/aliases/__init__.py

@abc.abstractmethod
def find_aliases_for_value_id(self, value_id: uuid.UUID) -> Optional[Set[str]]:
    pass

find_value_id_for_alias(self, alias)

Source code in kiara/registries/aliases/__init__.py

@abc.abstractmethod
def find_value_id_for_alias(self, alias: str) -> Optional[uuid.UUID]:
    pass

is_writeable() classmethod

Source code in kiara/registries/aliases/__init__.py

@classmethod
def is_writeable(cls) -> bool:
    return False

retrieve_all_aliases(self)

Retrieve a list of all aliases registered in this archive.

The result of this method can be 'None', for cases where the aliases are determined dynamically. In kiara, the result of this method is mostly used to improve performance when looking up an alias.

Returns:

Type	Description
Optional[Mapping[str, uuid.UUID]]	a list of strings (the aliases), or 'None' if this archive does not support alias indexes.

Source code in kiara/registries/aliases/__init__.py

@abc.abstractmethod
def retrieve_all_aliases(self) -> Optional[Mapping[str, uuid.UUID]]:
    """Retrieve a list of all aliases registered in this archive.

    The result of this method can be 'None', for cases where the aliases are determined dynamically.
    In kiara, the result of this method is mostly used to improve performance when looking up an alias.

    Returns:
        a list of strings (the aliases), or 'None' if this archive does not support alias indexes.
    """

supported_item_types() classmethod

Source code in kiara/registries/aliases/__init__.py

@classmethod
def supported_item_types(cls) -> Iterable[str]:
    return ["alias"]

AliasItem (tuple)

AliasItem(full_alias, rel_alias, value_id, alias_archive, alias_archive_id)

Source code in kiara/registries/aliases/__init__.py

class AliasItem(NamedTuple):
    full_alias: str
    rel_alias: str
    value_id: uuid.UUID
    alias_archive: str
    alias_archive_id: uuid.UUID

alias_archive: str

alias_archive_id: UUID

full_alias: str

rel_alias: str

value_id: UUID

AliasRegistry

Source code in kiara/registries/aliases/__init__.py

class AliasRegistry(object):
    def __init__(self, kiara: "Kiara"):

        self._kiara: Kiara = kiara

        self._event_callback: Callable = self._kiara.event_registry.add_producer(self)

        self._alias_archives: Dict[str, AliasArchive] = {}
        """All registered archives/stores."""

        self._default_alias_store: Optional[str] = None
        """The alias of the store where new aliases are stored by default."""

        self._cached_aliases: Optional[Dict[str, AliasItem]] = None
        self._cached_aliases_by_id: Optional[Dict[uuid.UUID, Set[AliasItem]]] = None

    def register_archive(
        self,
        archive: AliasArchive,
        alias: str = None,
        set_as_default_store: Optional[bool] = None,
    ):

        alias_archive_id = archive.register_archive(kiara=self._kiara)

        if alias is None:
            alias = str(alias_archive_id)

        if "." in alias:
            raise Exception(
                f"Can't register alias archive with as '{alias}': registered name is not allowed to contain a '.' character (yet)."
            )

        if alias in self._alias_archives.keys():
            raise Exception(f"Can't add store, alias '{alias}' already registered.")

        self._alias_archives[alias] = archive
        is_store = False
        is_default_store = False
        if isinstance(archive, AliasStore):
            is_store = True
            if set_as_default_store and self._default_alias_store is not None:
                raise Exception(
                    f"Can't set alias store '{alias}' as default store: default store already set."
                )

            if self._default_alias_store is None:
                is_default_store = True
                self._default_alias_store = alias

        event = AliasArchiveAddedEvent.construct(
            kiara_id=self._kiara.id,
            alias_archive_id=archive.archive_id,
            alias_archive_alias=alias,
            is_store=is_store,
            is_default_store=is_default_store,
        )
        self._event_callback(event)

    @property
    def default_alias_store(self) -> str:

        if self._default_alias_store is None:
            raise Exception("No default alias store set (yet).")
        return self._default_alias_store

    def get_archive(self, archive_id: Optional[str] = None) -> Optional[AliasArchive]:
        if archive_id is None:
            archive_id = self.default_alias_store
            if archive_id is None:
                raise Exception("Can't retrieve default alias archive, none set (yet).")

        archive = self._alias_archives.get(archive_id, None)
        return archive

    @property
    def all_aliases(self) -> Iterable[str]:

        return self.aliases.keys()

    @property
    def aliases_by_id(self) -> Mapping[uuid.UUID, Set[AliasItem]]:
        if self._cached_aliases_by_id is None:
            self.aliases  # noqa
        return self._cached_aliases_by_id  # type: ignore

    @property
    def aliases(self) -> Dict[str, AliasItem]:
        """Retrieve a map of all available aliases, context wide, with the registered archive aliases as values."""

        if self._cached_aliases is not None:
            return self._cached_aliases

        # TODO: multithreading lock

        all_aliases: Dict[str, AliasItem] = {}
        all_aliases_by_id: Dict[uuid.UUID, Set[AliasItem]] = {}
        for archive_alias, archive in self._alias_archives.items():
            alias_map = archive.retrieve_all_aliases()
            if alias_map is None:
                continue
            for alias, v_id in alias_map.items():
                if archive_alias == self.default_alias_store:
                    final_alias = alias
                else:
                    final_alias = f"{archive_alias}.{alias}"

                if final_alias in all_aliases.keys():
                    raise Exception(
                        f"Inconsistent alias registry: alias '{final_alias}' available more than once."
                    )
                item = AliasItem(
                    full_alias=final_alias,
                    rel_alias=alias,
                    value_id=v_id,
                    alias_archive=archive_alias,
                    alias_archive_id=archive.archive_id,
                )
                all_aliases[final_alias] = item
                all_aliases_by_id.setdefault(v_id, set()).add(item)

        self._cached_aliases = all_aliases
        self._cached_aliases_by_id = all_aliases_by_id
        return self._cached_aliases

    def find_value_id_for_alias(self, alias: str) -> Optional[uuid.UUID]:

        alias_item = self.aliases.get(alias, None)
        if alias_item is not None:
            return alias_item.value_id

        if "." not in alias:
            return None

        archive_id, rest = alias.split(".", maxsplit=2)
        archive = self.get_archive(archive_id=archive_id)

        if archive is None:
            # means no registered prefix
            archive = self.get_archive()
            assert archive is not None
            v_id = archive.find_value_id_for_alias(alias)
        else:
            v_id = archive.find_value_id_for_alias(alias=rest)

        # TODO: cache this?
        return v_id

    def find_aliases_for_value_id(
        self, value_id: uuid.UUID, search_dynamic_archives: bool = False
    ) -> Set[str]:

        aliases = set([a.full_alias for a in self.aliases_by_id.get(value_id, [])])

        if search_dynamic_archives:
            for archive_alias, archive in self._alias_archives.items():
                _aliases = archive.find_aliases_for_value_id(value_id=value_id)
                # TODO: cache those results
                if _aliases:
                    for a in _aliases:
                        aliases.add(f"{archive_alias}.{a}")

        return aliases

    def register_aliases(self, value_id: uuid.UUID, *aliases: str):

        store_name = self.default_alias_store
        store: AliasStore = self.get_archive(archive_id=store_name)  # type: ignore
        self.aliases  # noqu
        store.register_aliases(value_id, *aliases)

        for alias in aliases:
            alias_item = AliasItem(
                full_alias=alias,
                rel_alias=alias,
                value_id=value_id,
                alias_archive=store_name,
                alias_archive_id=store.archive_id,
            )

            if alias in self.aliases.keys():
                logger.info("alias.replace", alias=alias)
                # raise NotImplementedError()

            self.aliases[alias] = alias_item
            self._cached_aliases_by_id.setdefault(value_id, set()).add(alias_item)  # type: ignore

Attributes

aliases: Dict[str, kiara.registries.aliases.AliasItem] property readonly

Retrieve a map of all available aliases, context wide, with the registered archive aliases as values.

aliases_by_id: Mapping[uuid.UUID, Set[kiara.registries.aliases.AliasItem]] property readonly

all_aliases: Iterable[str] property readonly

default_alias_store: str property readonly

find_aliases_for_value_id(self, value_id, search_dynamic_archives=False)

Source code in kiara/registries/aliases/__init__.py

def find_aliases_for_value_id(
    self, value_id: uuid.UUID, search_dynamic_archives: bool = False
) -> Set[str]:

    aliases = set([a.full_alias for a in self.aliases_by_id.get(value_id, [])])

    if search_dynamic_archives:
        for archive_alias, archive in self._alias_archives.items():
            _aliases = archive.find_aliases_for_value_id(value_id=value_id)
            # TODO: cache those results
            if _aliases:
                for a in _aliases:
                    aliases.add(f"{archive_alias}.{a}")

    return aliases

find_value_id_for_alias(self, alias)

Source code in kiara/registries/aliases/__init__.py

def find_value_id_for_alias(self, alias: str) -> Optional[uuid.UUID]:

    alias_item = self.aliases.get(alias, None)
    if alias_item is not None:
        return alias_item.value_id

    if "." not in alias:
        return None

    archive_id, rest = alias.split(".", maxsplit=2)
    archive = self.get_archive(archive_id=archive_id)

    if archive is None:
        # means no registered prefix
        archive = self.get_archive()
        assert archive is not None
        v_id = archive.find_value_id_for_alias(alias)
    else:
        v_id = archive.find_value_id_for_alias(alias=rest)

    # TODO: cache this?
    return v_id

get_archive(self, archive_id=None)

Source code in kiara/registries/aliases/__init__.py

def get_archive(self, archive_id: Optional[str] = None) -> Optional[AliasArchive]:
    if archive_id is None:
        archive_id = self.default_alias_store
        if archive_id is None:
            raise Exception("Can't retrieve default alias archive, none set (yet).")

    archive = self._alias_archives.get(archive_id, None)
    return archive

register_aliases(self, value_id, *aliases)

Source code in kiara/registries/aliases/__init__.py

def register_aliases(self, value_id: uuid.UUID, *aliases: str):

    store_name = self.default_alias_store
    store: AliasStore = self.get_archive(archive_id=store_name)  # type: ignore
    self.aliases  # noqu
    store.register_aliases(value_id, *aliases)

    for alias in aliases:
        alias_item = AliasItem(
            full_alias=alias,
            rel_alias=alias,
            value_id=value_id,
            alias_archive=store_name,
            alias_archive_id=store.archive_id,
        )

        if alias in self.aliases.keys():
            logger.info("alias.replace", alias=alias)
            # raise NotImplementedError()

        self.aliases[alias] = alias_item
        self._cached_aliases_by_id.setdefault(value_id, set()).add(alias_item)  # type: ignore

register_archive(self, archive, alias=None, set_as_default_store=None)

Source code in kiara/registries/aliases/__init__.py

def register_archive(
    self,
    archive: AliasArchive,
    alias: str = None,
    set_as_default_store: Optional[bool] = None,
):

    alias_archive_id = archive.register_archive(kiara=self._kiara)

    if alias is None:
        alias = str(alias_archive_id)

    if "." in alias:
        raise Exception(
            f"Can't register alias archive with as '{alias}': registered name is not allowed to contain a '.' character (yet)."
        )

    if alias in self._alias_archives.keys():
        raise Exception(f"Can't add store, alias '{alias}' already registered.")

    self._alias_archives[alias] = archive
    is_store = False
    is_default_store = False
    if isinstance(archive, AliasStore):
        is_store = True
        if set_as_default_store and self._default_alias_store is not None:
            raise Exception(
                f"Can't set alias store '{alias}' as default store: default store already set."
            )

        if self._default_alias_store is None:
            is_default_store = True
            self._default_alias_store = alias

    event = AliasArchiveAddedEvent.construct(
        kiara_id=self._kiara.id,
        alias_archive_id=archive.archive_id,
        alias_archive_alias=alias,
        is_store=is_store,
        is_default_store=is_default_store,
    )
    self._event_callback(event)

AliasStore (AliasArchive)

Source code in kiara/registries/aliases/__init__.py

class AliasStore(AliasArchive):
    @abc.abstractmethod
    def register_aliases(self, value_id: uuid.UUID, *aliases: str):
        pass

register_aliases(self, value_id, *aliases)

Source code in kiara/registries/aliases/__init__.py

@abc.abstractmethod
def register_aliases(self, value_id: uuid.UUID, *aliases: str):
    pass

Modules

archives

Classes

FileSystemAliasArchive (AliasArchive)

Source code in kiara/registries/aliases/archives.py

class FileSystemAliasArchive(AliasArchive):

    _archive_type_name = "filesystem_alias_archive"
    _config_cls = FileSystemArchiveConfig

    def __init__(self, archive_id: uuid.UUID, config: ARCHIVE_CONFIG_CLS):

        super().__init__(archive_id=archive_id, config=config)

        self._base_path: Optional[Path] = None

    @property
    def alias_store_path(self) -> Path:

        if self._base_path is not None:
            return self._base_path

        self._base_path = Path(self.config.archive_path).absolute()
        self._base_path.mkdir(parents=True, exist_ok=True)
        return self._base_path

    @property
    def aliases_path(self) -> Path:
        return self.alias_store_path / "aliases"

    @property
    def value_id_path(self) -> Path:
        return self.alias_store_path / "value_ids"

    def _translate_alias(self, alias: str) -> Path:

        if "." in alias:
            tokens = alias.split(".")
            alias_path = (
                self.aliases_path.joinpath(*tokens[0:-1]) / f"{tokens[-1]}.alias"
            )
        else:
            alias_path = self.aliases_path / f"{alias}.alias"
        return alias_path

    def _translate_alias_path(self, alias_path: Path) -> str:

        relative = (
            alias_path.absolute()
            .relative_to(self.aliases_path.absolute())
            .as_posix()[:-6]
        )

        relative = os.path.normpath(relative)

        if os.path.sep not in relative:
            alias = relative
        else:
            alias = ".".join(relative.split(os.path.sep))

        return alias

    def _translate_value_id(self, value_id: uuid.UUID) -> Path:

        tokens = str(value_id).split("-")
        value_id_path = (
            self.value_id_path.joinpath(*tokens[0:-1]) / f"{tokens[-1]}.value"
        )
        return value_id_path

    def _translate_value_path(self, value_path: Path) -> uuid.UUID:

        relative = (
            value_path.absolute()
            .relative_to(self.value_id_path.absolute())
            .as_posix()[:-6]
        )
        relative = os.path.normpath(relative)
        value_id_str = "-".join(relative.split(os.path.sep))

        return uuid.UUID(value_id_str)

    def retrieve_all_aliases(self) -> Mapping[str, uuid.UUID]:

        all_aliases = self.aliases_path.rglob("*.alias")
        result = {}
        for alias_path in all_aliases:
            alias = self._translate_alias_path(alias_path=alias_path)
            value_id = self._find_value_id_for_alias_path(alias_path=alias_path)
            assert value_id is not None
            result[alias] = value_id

        return result

    def find_value_id_for_alias(self, alias: str) -> Optional[uuid.UUID]:
        alias_path = self._translate_alias(alias)
        if not alias_path.exists():
            return None
        return self._find_value_id_for_alias_path(alias_path=alias_path)

    def _find_value_id_for_alias_path(self, alias_path: Path) -> Optional[uuid.UUID]:

        resolved = alias_path.resolve()

        assert resolved.name.endswith(".value")

        value_id = self._translate_value_path(value_path=resolved)
        return value_id

    def find_aliases_for_value_id(self, value_id: uuid.UUID) -> Optional[Set[str]]:
        raise NotImplementedError()

alias_store_path: Path property readonly

aliases_path: Path property readonly

value_id_path: Path property readonly

Classes

_config_cls (ArchiveConfig) private pydantic-model

Source code in kiara/registries/aliases/archives.py

class FileSystemArchiveConfig(ArchiveConfig):

    archive_path: str = Field(
        description="The path where the data for this archive is stored."
    )

Attributes

archive_path: str pydantic-field required

The path where the data for this archive is stored.

Methods

find_aliases_for_value_id(self, value_id)

Source code in kiara/registries/aliases/archives.py

def find_aliases_for_value_id(self, value_id: uuid.UUID) -> Optional[Set[str]]:
    raise NotImplementedError()

find_value_id_for_alias(self, alias)

Source code in kiara/registries/aliases/archives.py

def find_value_id_for_alias(self, alias: str) -> Optional[uuid.UUID]:
    alias_path = self._translate_alias(alias)
    if not alias_path.exists():
        return None
    return self._find_value_id_for_alias_path(alias_path=alias_path)

retrieve_all_aliases(self)

Retrieve a list of all aliases registered in this archive.

The result of this method can be 'None', for cases where the aliases are determined dynamically. In kiara, the result of this method is mostly used to improve performance when looking up an alias.

Returns:

Type	Description
Mapping[str, uuid.UUID]	a list of strings (the aliases), or 'None' if this archive does not support alias indexes.

Source code in kiara/registries/aliases/archives.py

def retrieve_all_aliases(self) -> Mapping[str, uuid.UUID]:

    all_aliases = self.aliases_path.rglob("*.alias")
    result = {}
    for alias_path in all_aliases:
        alias = self._translate_alias_path(alias_path=alias_path)
        value_id = self._find_value_id_for_alias_path(alias_path=alias_path)
        assert value_id is not None
        result[alias] = value_id

    return result

FileSystemAliasStore (FileSystemAliasArchive, AliasStore)

Source code in kiara/registries/aliases/archives.py

class FileSystemAliasStore(FileSystemAliasArchive, AliasStore):

    _archive_type_name = "filesystem_alias_store"

    @classmethod
    def is_writeable(cls) -> bool:
        return True

    def register_aliases(self, value_id: uuid.UUID, *aliases: str):

        value_path = self._translate_value_id(value_id=value_id)
        value_path.parent.mkdir(parents=True, exist_ok=True)
        value_path.touch()

        for alias in aliases:
            alias_path = self._translate_alias(alias)
            alias_path.parent.mkdir(parents=True, exist_ok=True)
            if alias_path.exists():
                resolved = alias_path.resolve()
                if resolved == value_path:
                    continue
                alias_path.unlink()
            alias_path.symlink_to(value_path)

is_writeable() classmethod

Source code in kiara/registries/aliases/archives.py

@classmethod
def is_writeable(cls) -> bool:
    return True

register_aliases(self, value_id, *aliases)

Source code in kiara/registries/aliases/archives.py

def register_aliases(self, value_id: uuid.UUID, *aliases: str):

    value_path = self._translate_value_id(value_id=value_id)
    value_path.parent.mkdir(parents=True, exist_ok=True)
    value_path.touch()

    for alias in aliases:
        alias_path = self._translate_alias(alias)
        alias_path.parent.mkdir(parents=True, exist_ok=True)
        if alias_path.exists():
            resolved = alias_path.resolve()
            if resolved == value_path:
                continue
            alias_path.unlink()
        alias_path.symlink_to(value_path)

data special

NONE_PERSISTED_DATA

logger

Classes

DataRegistry

Source code in kiara/registries/data/__init__.py

class DataRegistry(object):
    def __init__(self, kiara: "Kiara"):

        self._kiara: Kiara = kiara

        self._event_callback: Callable = self._kiara.event_registry.add_producer(self)

        self._data_archives: Dict[str, DataArchive] = {}

        self._default_data_store: Optional[str] = None
        self._registered_values: Dict[uuid.UUID, Value] = {}

        self._value_archive_lookup_map: Dict[uuid.UUID, str] = {}

        self._values_by_hash: Dict[str, Set[uuid.UUID]] = {}

        self._cached_data: Dict[uuid.UUID, Any] = {}
        self._persisted_value_descs: Dict[uuid.UUID, Optional[PersistedData]] = {}

        # initialize special values
        special_value_cls = PythonClass.from_class(NoneType)
        self._not_set_value: Value = Value(
            value_id=NOT_SET_VALUE_ID,
            kiara_id=self._kiara.id,
            value_schema=ValueSchema(
                type="none",
                default=SpecialValue.NOT_SET,
                is_constant=True,
                doc="Special value, indicating a field is not set.",  # type: ignore
            ),
            value_status=ValueStatus.NOT_SET,
            value_size=0,
            value_hash=INVALID_HASH_MARKER,
            pedigree=ORPHAN,
            pedigree_output_name="__void__",
            data_type_class=special_value_cls,
        )
        self._not_set_value._data_registry = self
        self._cached_data[NOT_SET_VALUE_ID] = SpecialValue.NOT_SET
        self._registered_values[NOT_SET_VALUE_ID] = self._not_set_value
        self._persisted_value_descs[NOT_SET_VALUE_ID] = NONE_PERSISTED_DATA

        self._none_value: Value = Value(
            value_id=NONE_VALUE_ID,
            kiara_id=self._kiara.id,
            value_schema=ValueSchema(
                type="special_type",
                default=SpecialValue.NO_VALUE,
                is_constant=True,
                doc="Special value, indicating a field is set with a 'none' value.",  # type: ignore
            ),
            value_status=ValueStatus.NONE,
            value_size=0,
            value_hash=-2,
            pedigree=ORPHAN,
            pedigree_output_name="__void__",
            data_type_class=special_value_cls,
        )
        self._none_value._data_registry = self
        self._cached_data[NONE_VALUE_ID] = SpecialValue.NO_VALUE
        self._registered_values[NONE_VALUE_ID] = self._none_value
        self._persisted_value_descs[NONE_VALUE_ID] = NONE_PERSISTED_DATA

    @property
    def kiara_id(self) -> uuid.UUID:
        return self._kiara.id

    @property
    def NOT_SET_VALUE(self) -> Value:
        return self._not_set_value

    @property
    def NONE_VALUE(self) -> Value:
        return self._none_value

    def retrieve_all_available_value_ids(self) -> Set[uuid.UUID]:

        result: Set[uuid.UUID] = set()
        for store in self._data_archives.values():
            ids = store.value_ids
            result.update(ids)

        return result

    def register_data_archive(
        self,
        archive: DataArchive,
        alias: str = None,
        set_as_default_store: Optional[bool] = None,
    ):

        data_store_id = archive.register_archive(kiara=self._kiara)
        if alias is None:
            alias = str(data_store_id)

        if alias in self._data_archives.keys():
            raise Exception(f"Can't add store, alias '{alias}' already registered.")
        self._data_archives[alias] = archive
        is_store = False
        is_default_store = False
        if isinstance(archive, DataStore):
            is_store = True

            if set_as_default_store and self._default_data_store is not None:
                raise Exception(
                    f"Can't set data store '{alias}' as default store: default store already set."
                )

            if self._default_data_store is None or set_as_default_store:
                is_default_store = True
                self._default_data_store = alias

        event = DataArchiveAddedEvent.construct(
            kiara_id=self._kiara.id,
            data_archive_id=archive.archive_id,
            data_archive_alias=alias,
            is_store=is_store,
            is_default_store=is_default_store,
        )
        self._event_callback(event)

    @property
    def default_data_store(self) -> str:
        if self._default_data_store is None:
            raise Exception("No default data store set.")
        return self._default_data_store

    @property
    def data_archives(self) -> Mapping[str, DataArchive]:
        return self._data_archives

    def get_archive(
        self, archive_id: Union[None, uuid.UUID, str] = None
    ) -> DataArchive:

        if archive_id is None:
            archive_id = self.default_data_store
            if archive_id is None:
                raise Exception("Can't retrieve default data archive, none set (yet).")

        if isinstance(archive_id, uuid.UUID):
            for archive in self._data_archives.values():
                if archive.archive_id == archive_id:
                    return archive

            raise Exception(
                f"Can't retrieve archive with id '{archive_id}': no archive with that id registered."
            )

        if archive_id in self._data_archives.keys():
            return self._data_archives[archive_id]
        else:
            try:
                _archive_id = uuid.UUID(archive_id)
                for archive in self._data_archives.values():
                    if archive.archive_id == _archive_id:
                        return archive
                    raise Exception(
                        f"Can't retrieve archive with id '{archive_id}': no archive with that id registered."
                    )
            except Exception:
                pass

        raise Exception(
            f"Can't retrieve archive with id '{archive_id}': no archive with that id registered."
        )

    def find_store_id_for_value(self, value_id: uuid.UUID) -> Optional[str]:

        if value_id in self._value_archive_lookup_map.keys():
            return self._value_archive_lookup_map[value_id]

        matches = []
        for store_id, store in self.data_archives.items():
            match = store.has_value(value_id=value_id)
            if match:
                matches.append(store_id)

        if len(matches) == 0:
            return None
        elif len(matches) > 1:
            raise Exception(
                f"Found value with id '{value_id}' in multiple archives, this is not supported (yet): {matches}"
            )

        self._value_archive_lookup_map[value_id] = matches[0]
        return matches[0]

    def get_value(self, value_id: Union[uuid.UUID, Value, str]) -> Value:
        _value_id = None
        if not isinstance(value_id, uuid.UUID):
            # fallbacks for common mistakes, this should error out if not a Value or string.
            if hasattr(value_id, "value_id"):
                _value_id: Optional[uuid.UUID] = value_id.value_id  # type: ignore
            else:

                try:
                    _value_id = uuid.UUID(
                        value_id  # type: ignore
                    )  # this should fail if not string or wrong string format
                except ValueError:
                    _value_id = None

                if _value_id is None:

                    if not isinstance(value_id, str):
                        raise Exception(
                            f"Can't retrieve value for '{value_id}': invalid type '{type(value_id)}'."
                        )

                    if ":" not in value_id:
                        raise Exception(
                            f"Can't retrieve value for '{value_id}': can't determine reference type."
                        )

                    ref_type, rest = value_id.split(":", maxsplit=1)

                    if ref_type == "value":
                        _value_id = uuid.UUID(rest)
                    elif ref_type == "alias":
                        _value_id = self._kiara.alias_registry.find_value_id_for_alias(
                            alias=rest
                        )
                        if _value_id is None:
                            raise NoSuchValueAliasException(
                                alias=rest,
                                msg=f"Can't retrive value for alias '{rest}': no such alias registered.",
                            )
                    else:
                        raise Exception(
                            f"Can't retrieve value for '{value_id}': invalid reference type '{ref_type}'."
                        )
        else:
            _value_id = value_id

        assert _value_id is not None

        if _value_id in self._registered_values.keys():
            value = self._registered_values[_value_id]
            return value

        matches = []
        for store_id, store in self.data_archives.items():
            match = store.has_value(value_id=_value_id)
            if match:
                matches.append(store_id)

        if len(matches) == 0:
            raise NoSuchValueIdException(
                value_id=_value_id, msg=f"No value registered with id: {value_id}"
            )
        elif len(matches) > 1:
            raise NoSuchValueIdException(
                value_id=_value_id,
                msg=f"Found value with id '{value_id}' in multiple archives, this is not supported (yet): {matches}",
            )

        self._value_archive_lookup_map[_value_id] = matches[0]
        stored_value = self.get_archive(matches[0]).retrieve_value(value_id=_value_id)
        stored_value._set_registry(self)
        stored_value._is_stored = True

        self._registered_values[_value_id] = stored_value
        return self._registered_values[_value_id]

    def store_value(
        self,
        value: Union[Value, uuid.UUID],
        store_id: Optional[str] = None,
    ) -> Optional[PersistedData]:

        if store_id is None:
            store_id = self.default_data_store

        if isinstance(value, uuid.UUID):
            value = self.get_value(value)

        store: DataStore = self.get_archive(archive_id=store_id)  # type: ignore
        if not isinstance(store, DataStore):
            raise Exception(f"Can't store value into store '{store_id}': not writable.")

        # make sure all property values are available
        if value.pedigree != ORPHAN:
            for value_id in value.pedigree.inputs.values():
                self.store_value(value=value_id, store_id=store_id)

        if not store.has_value(value.value_id):
            event = ValuePreStoreEvent.construct(kiara_id=self._kiara.id, value=value)
            self._event_callback(event)
            persisted_value = store.store_value(value)
            value._is_stored = True
            self._value_archive_lookup_map[value.value_id] = store_id
            self._persisted_value_descs[value.value_id] = persisted_value
            property_values = value.property_values

            for property, property_value in property_values.items():
                self.store_value(value=property_value, store_id=store_id)
        else:
            persisted_value = None

        store_event = ValueStoredEvent.construct(kiara_id=self._kiara.id, value=value)
        self._event_callback(store_event)

        return persisted_value

    def find_values_for_hash(
        self, value_hash: str, data_type_name: Optional[str] = None
    ) -> Set[Value]:

        if data_type_name:
            raise NotImplementedError()

        stored = self._values_by_hash.get(value_hash, None)
        if stored is None:
            matches: Dict[uuid.UUID, List[str]] = {}
            for store_id, store in self.data_archives.items():
                value_ids = store.find_values_with_hash(
                    value_hash=value_hash, data_type_name=data_type_name
                )
                for v_id in value_ids:
                    matches.setdefault(v_id, []).append(store_id)

            stored = set()
            for v_id, store_ids in matches.items():
                if len(store_ids) > 1:
                    raise Exception(
                        f"Found multiple stores for value id '{v_id}', this is not supported (yet)."
                    )
                self._value_archive_lookup_map[v_id] = store_ids[0]
                stored.add(v_id)

            if stored:
                self._values_by_hash[value_hash] = stored

        return set((self.get_value(value_id=v_id) for v_id in stored))

    def find_destinies_for_value(
        self, value_id: uuid.UUID, alias_filter: str = None
    ) -> Mapping[str, uuid.UUID]:

        if alias_filter:
            raise NotImplementedError()

        all_destinies: Dict[str, uuid.UUID] = {}
        for archive_id, archive in self._data_archives.items():
            destinies: Optional[
                Mapping[str, uuid.UUID]
            ] = archive.find_destinies_for_value(
                value_id=value_id, alias_filter=alias_filter
            )
            if not destinies:
                continue
            for k, v in destinies.items():
                if k in all_destinies.keys():
                    raise Exception(f"Duplicate destiny '{k}' for value '{value_id}'.")
                all_destinies[k] = v

        return all_destinies

    def register_data(
        self,
        data: Any,
        schema: Union[ValueSchema, str] = None,
        pedigree: Optional[ValuePedigree] = None,
        pedigree_output_name: str = None,
        reuse_existing: bool = True,
    ) -> Value:

        value, newly_created = self._create_value(
            data=data,
            schema=schema,
            pedigree=pedigree,
            pedigree_output_name=pedigree_output_name,
            reuse_existing=reuse_existing,
        )

        if newly_created:
            self._values_by_hash.setdefault(value.value_hash, set()).add(value.value_id)
            self._registered_values[value.value_id] = value
            self._cached_data[value.value_id] = data

            event = ValueRegisteredEvent(kiara_id=self._kiara.id, value=value)
            self._event_callback(event)

        return value

    def _find_existing_value(
        self, data: Any, schema: Optional[ValueSchema]
    ) -> Tuple[
        Optional[Value],
        DataType,
        Optional[Any],
        Union[str, SerializedData],
        ValueStatus,
        str,
        int,
    ]:

        if schema is None:
            raise NotImplementedError()

        if isinstance(data, Value):

            if data.value_id in self._registered_values.keys():

                if data.is_set and data.is_serializable:
                    serialized: Union[str, SerializedData] = data.serialized_data
                else:
                    serialized = NO_SERIALIZATION_MARKER
                return (
                    data,
                    data.data_type,
                    None,
                    serialized,
                    data.value_status,
                    data.value_hash,
                    data.value_size,
                )

            raise NotImplementedError("Importing values not supported (yet).")
            # self._registered_values[data.value_id] = data
            # return data

        try:
            value = self.get_value(value_id=data)
            if value.is_serializable:
                serialized = value.serialized_data
            else:
                serialized = NO_SERIALIZATION_MARKER

            return (
                value,
                value.data_type,
                None,
                serialized,
                value.value_status,
                value.value_hash,
                value.value_size,
            )
        except NoSuchValueException as nsve:
            raise nsve
        except Exception:
            # TODO: differentiate between 'value not found' and other type of errors
            pass

        # no obvious matches, so we try to find data that has the same hash
        data_type = self._kiara.type_registry.retrieve_data_type(
            data_type_name=schema.type, data_type_config=schema.type_config
        )

        data, serialized, status, value_hash, value_size = data_type._pre_examine_data(
            data=data, schema=schema
        )

        existing_value: Optional[Value] = None
        if value_hash != INVALID_HASH_MARKER:
            existing = self.find_values_for_hash(value_hash=value_hash)
            if existing:
                if len(existing) == 1:
                    existing_value = next(iter(existing))
                else:
                    skalars = []
                    for v in existing:
                        if v.data_type.characteristics.is_scalar:
                            skalars.append(v)

                    if len(skalars) == 1:
                        existing_value = skalars[0]
                    elif skalars:
                        orphans = []
                        for v in skalars:
                            if v.pedigree == ORPHAN:
                                orphans.append(v)

                        if len(orphans) == 1:
                            existing_value = orphans[0]

        if existing_value is not None:
            self._persisted_value_descs[existing_value.value_id] = None
            return (
                existing_value,
                data_type,
                data,
                serialized,
                status,
                value_hash,
                value_size,
            )

        return (None, data_type, data, serialized, status, value_hash, value_size)

    def _create_value(
        self,
        data: Any,
        schema: Union[None, str, ValueSchema] = None,
        pedigree: Optional[ValuePedigree] = None,
        pedigree_output_name: str = None,
        reuse_existing: bool = True,
    ) -> Tuple[Value, bool]:
        """Create a new value, or return an existing one that matches the incoming data or reference.

        Arguments:
            data: the (raw) data, or a reference to an existing value


        Returns:
            a tuple containing of the value object, and a boolean indicating whether the value was newly created (True), or already existing (False)
        """

        if schema is None:
            raise NotImplementedError()
        elif isinstance(schema, str):
            schema = ValueSchema(type=schema)

        if schema.type not in self._kiara.data_type_names:
            raise Exception(
                f"Can't register data of type '{schema.type}': type not registered. Available types: {', '.join(self._kiara.data_type_names)}"
            )

        if schema.default in [None, SpecialValue.NO_VALUE, SpecialValue.NOT_SET]:
            if data in [None, SpecialValue.NO_VALUE]:
                return (self.NONE_VALUE, False)
            elif data is SpecialValue.NOT_SET:
                return (self.NOT_SET_VALUE, False)
        else:
            # TODO: allow other value_ids in defaults?
            if data in [None, SpecialValue.NO_VALUE, SpecialValue.NOT_SET]:
                if callable(schema.default):
                    data = schema.default()
                else:
                    data = copy.deepcopy(schema.default)

        # data_type: Optional[DataType] = None
        # status: Optional[ValueStatus] = None
        # value_hash: Optional[str] = None

        if reuse_existing:
            (
                _existing,
                data_type,
                data,
                serialized,
                status,
                value_hash,
                value_size,
            ) = self._find_existing_value(data=data, schema=schema)
            if _existing is not None:
                # TODO: check pedigree
                return (_existing, False)
        else:

            data_type = self._kiara.type_registry.retrieve_data_type(
                data_type_name=schema.type, data_type_config=schema.type_config
            )

            (
                data,
                serialized,
                status,
                value_hash,
                value_size,
            ) = data_type._pre_examine_data(data=data, schema=schema)

        if pedigree is None:
            pedigree = ORPHAN

        if pedigree_output_name is None:
            if pedigree == ORPHAN:
                pedigree_output_name = ORPHAN_PEDIGREE_OUTPUT_NAME
            else:
                raise NotImplementedError()

        v_id = ID_REGISTRY.generate(
            type="value", kiara_id=self._kiara.id, pre_registered=False
        )

        value, data = data_type.assemble_value(
            value_id=v_id,
            data=data,
            schema=schema,
            serialized=serialized,
            status=status,
            value_hash=value_hash,
            value_size=value_size,
            pedigree=pedigree,
            kiara_id=self._kiara.id,
            pedigree_output_name=pedigree_output_name,
        )
        ID_REGISTRY.update_metadata(v_id, obj=value)
        value._data_registry = self

        event = ValueCreatedEvent(kiara_id=self._kiara.id, value=value)
        self._event_callback(event)

        return (value, True)

    def retrieve_persisted_value_details(self, value_id: uuid.UUID) -> PersistedData:

        if (
            value_id in self._persisted_value_descs.keys()
            and self._persisted_value_descs[value_id] is not None
        ):
            persisted_details = self._persisted_value_descs[value_id]
            assert persisted_details is not None
        else:
            # now, the value_store map should contain this value_id
            store_id = self.find_store_id_for_value(value_id=value_id)
            if store_id is None:
                raise Exception(
                    f"Can't find store for persisted data of value: {value_id}"
                )

            store = self.get_archive(store_id)
            assert value_id in self._registered_values.keys()
            # self.get_value(value_id=value_id)
            persisted_details = store.retrieve_serialized_value(value=value_id)
            for c in persisted_details.chunk_id_map.values():
                c._data_registry = self._kiara.data_registry
            self._persisted_value_descs[value_id] = persisted_details

        return persisted_details

    # def _retrieve_bytes(
    #     self, chunk_id: str, as_link: bool = True
    # ) -> Union[str, bytes]:
    #
    #     # TODO: support multiple stores
    #     return self.get_archive().retrieve_chunk(chunk_id=chunk_id, as_link=as_link)

    def retrieve_serialized_value(
        self, value_id: uuid.UUID
    ) -> Optional[SerializedData]:
        """Create a LoadConfig object from the details of the persisted version of this value."""

        pv = self.retrieve_persisted_value_details(value_id=value_id)
        if pv is None:
            return None

        return pv

    def retrieve_chunk(
        self,
        chunk_id: str,
        archive_id: Optional[uuid.UUID] = None,
        as_file: Union[None, bool, str] = None,
        symlink_ok: bool = True,
    ) -> Union[str, bytes]:

        if archive_id is None:
            raise NotImplementedError()

        archive = self.get_archive(archive_id)
        chunk = archive.retrieve_chunk(chunk_id, as_file=as_file, symlink_ok=symlink_ok)

        return chunk

    def retrieve_value_data(
        self, value: Union[uuid.UUID, Value], target_profile: Optional[str] = None
    ) -> Any:

        if isinstance(value, uuid.UUID):
            value = self.get_value(value_id=value)

        if value.value_id in self._cached_data.keys():
            return self._cached_data[value.value_id]

        if value._serialized_data is None:
            serialized_data: Union[
                str, SerializedData
            ] = self.retrieve_persisted_value_details(value_id=value.value_id)
            value._serialized_data = serialized_data
        else:
            serialized_data = value._serialized_data

        if isinstance(serialized_data, str):
            raise Exception(
                f"Can't retrieve serialized version of value '{value.value_id}', this is most likely a bug."
            )

        manifest = serialized_data.metadata.deserialize.get("python_object", None)
        if manifest is None:
            raise Exception(
                f"No deserialize operation found for data type: {value.data_type_name}"
            )

        module = self._kiara.create_module(manifest=manifest)
        op = Operation.create_from_module(module=module)

        input_field_match: Optional[str] = None
        if len(op.inputs_schema) == 1:
            input_field_match = next(iter(op.inputs_schema.keys()))
        else:
            for input_field, schema in op.inputs_schema.items():
                if schema.type == value.data_type_name:
                    if input_field_match is not None:
                        raise Exception(
                            f"Can't determine input field for deserialization operation '{module.module_type_name}': multiple input fields with type '{input_field_match}'."
                        )
                    else:
                        input_field_match = input_field
        if input_field_match is None:
            raise Exception(
                f"Can't determine input field for deserialization operation '{module.module_type_name}'."
            )

        result_field_match: Optional[str] = None
        for result_field, schema in op.outputs_schema.items():
            if schema.type == "python_object":
                if result_field_match is not None:
                    raise Exception(
                        f"Can't determine result field for deserialization operation '{module.module_type_name}': multiple result fields with type 'python_object'."
                    )
                else:
                    result_field_match = result_field
        if result_field_match is None:
            raise Exception(
                f"Can't determine result field for deserialization operation '{module.module_type_name}'."
            )

        inputs = {input_field_match: value}

        result = op.run(kiara=self._kiara, inputs=inputs)
        python_object = result.get_value_data(result_field_match)
        self._cached_data[value.value_id] = python_object

        return python_object

        # op_type: DeSerializeOperationType = self._kiara.operation_registry.get_operation_type("deserialize")  # type: ignore
        # ops = op_type.find_deserialzation_operation_for_type_and_profile(
        #     serialized_data.data_type, serialized_data.serialization_profile
        # )
        #
        # if len(ops) > 1:
        #     raise Exception("No unique op.")
        #
        # if not ops:
        #     raise Exception(
        #         f"No deserialize operation found for data type: {value.data_type_name}"
        #     )
        #
        # op = ops[0]
        # inputs = {"value": serialized_data}
        #
        # result = op.run(kiara=self._kiara, inputs=inputs)
        #
        # python_object = result.get_value_data("python_object")
        # self._cached_data[value.value_id] = python_object
        #
        # return python_object

    def load_values(self, values: Mapping[str, Optional[uuid.UUID]]) -> ValueMap:

        value_items = {}
        schemas = {}
        for field_name, value_id in values.items():
            if value_id is None:
                value_id = NONE_VALUE_ID

            value = self.get_value(value_id=value_id)
            value_items[field_name] = value
            schemas[field_name] = value.value_schema

        return ValueMapReadOnly(value_items=value_items, values_schema=schemas)

    def load_data(self, values: Mapping[str, Optional[uuid.UUID]]) -> Mapping[str, Any]:

        result_values = self.load_values(values=values)
        return {k: v.data for k, v in result_values.items()}

    def create_valueset(
        self, data: Mapping[str, Any], schema: Mapping[str, ValueSchema]
    ) -> ValueMap:
        """Extract a set of [Value][kiara.data.values.Value] from Python data and ValueSchemas."""

        input_details = {}
        for input_name, value_schema in schema.items():
            input_details[input_name] = {"schema": value_schema}

        leftover = set(data.keys())
        leftover.difference_update(input_details.keys())
        if leftover:
            if not STRICT_CHECKS:
                log_message("unused.inputs", input_names=leftover)
            else:
                raise Exception(
                    f"Can't create values instance, inputs contain unused/invalid fields: {', '.join(leftover)}"
                )

        values = {}

        failed = {}
        for input_name, details in input_details.items():

            value_schema = details["schema"]

            if input_name not in data.keys():
                value_data = SpecialValue.NOT_SET
            elif data[input_name] in [
                None,
                SpecialValue.NO_VALUE,
                SpecialValue.NOT_SET,
            ]:
                value_data = SpecialValue.NO_VALUE
            else:
                value_data = data[input_name]

            try:
                value = self.register_data(
                    data=value_data, schema=value_schema, reuse_existing=True
                )
                # value = self.retrieve_or_create_value(
                #     value_data, value_schema=value_schema
                # )
                values[input_name] = value
            except Exception as e:

                log_exception(e)

                msg: Any = str(e)
                if not msg:
                    msg = e
                log_message("invalid.valueset", error_reason=msg, input_name=input_name)
                failed[input_name] = e

        if failed:
            msg = []
            for k, v in failed.items():
                _v = str(v)
                if not str(v):
                    _v = type(v).__name__
                msg.append(f"{k}: {_v}")
            raise InvalidValuesException(
                msg=f"Can't create values instance: {', '.join(msg)}",
                invalid_values={k: str(v) for k, v in failed.items()},
            )
        return ValueMapReadOnly(value_items=values, values_schema=schema)  # type: ignore

    def create_renderable(self, **config: Any) -> RenderableType:
        """Create a renderable for this module configuration."""

        from kiara.utils.output import create_renderable_from_values

        all_values = {str(i): v for i, v in self._registered_values.items()}

        table = create_renderable_from_values(values=all_values, config=config)
        return table

    def pretty_print_data(
        self,
        value_id: uuid.UUID,
        target_type="terminal_renderable",
        **render_config: Any,
    ) -> Any:

        assert isinstance(value_id, uuid.UUID)

        return pretty_print_data(
            kiara=self._kiara,
            value_id=value_id,
            target_type=target_type,
            **render_config,
        )

NONE_VALUE: Value property readonly

NOT_SET_VALUE: Value property readonly

data_archives: Mapping[str, kiara.registries.data.data_store.DataArchive] property readonly

default_data_store: str property readonly

kiara_id: UUID property readonly

Methods

create_renderable(self, **config)

Create a renderable for this module configuration.

Source code in kiara/registries/data/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:
    """Create a renderable for this module configuration."""

    from kiara.utils.output import create_renderable_from_values

    all_values = {str(i): v for i, v in self._registered_values.items()}

    table = create_renderable_from_values(values=all_values, config=config)
    return table

create_valueset(self, data, schema)

Extract a set of [Value][kiara.data.values.Value] from Python data and ValueSchemas.

Source code in kiara/registries/data/__init__.py

def create_valueset(
    self, data: Mapping[str, Any], schema: Mapping[str, ValueSchema]
) -> ValueMap:
    """Extract a set of [Value][kiara.data.values.Value] from Python data and ValueSchemas."""

    input_details = {}
    for input_name, value_schema in schema.items():
        input_details[input_name] = {"schema": value_schema}

    leftover = set(data.keys())
    leftover.difference_update(input_details.keys())
    if leftover:
        if not STRICT_CHECKS:
            log_message("unused.inputs", input_names=leftover)
        else:
            raise Exception(
                f"Can't create values instance, inputs contain unused/invalid fields: {', '.join(leftover)}"
            )

    values = {}

    failed = {}
    for input_name, details in input_details.items():

        value_schema = details["schema"]

        if input_name not in data.keys():
            value_data = SpecialValue.NOT_SET
        elif data[input_name] in [
            None,
            SpecialValue.NO_VALUE,
            SpecialValue.NOT_SET,
        ]:
            value_data = SpecialValue.NO_VALUE
        else:
            value_data = data[input_name]

        try:
            value = self.register_data(
                data=value_data, schema=value_schema, reuse_existing=True
            )
            # value = self.retrieve_or_create_value(
            #     value_data, value_schema=value_schema
            # )
            values[input_name] = value
        except Exception as e:

            log_exception(e)

            msg: Any = str(e)
            if not msg:
                msg = e
            log_message("invalid.valueset", error_reason=msg, input_name=input_name)
            failed[input_name] = e

    if failed:
        msg = []
        for k, v in failed.items():
            _v = str(v)
            if not str(v):
                _v = type(v).__name__
            msg.append(f"{k}: {_v}")
        raise InvalidValuesException(
            msg=f"Can't create values instance: {', '.join(msg)}",
            invalid_values={k: str(v) for k, v in failed.items()},
        )
    return ValueMapReadOnly(value_items=values, values_schema=schema)  # type: ignore

find_destinies_for_value(self, value_id, alias_filter=None)

Source code in kiara/registries/data/__init__.py

def find_destinies_for_value(
    self, value_id: uuid.UUID, alias_filter: str = None
) -> Mapping[str, uuid.UUID]:

    if alias_filter:
        raise NotImplementedError()

    all_destinies: Dict[str, uuid.UUID] = {}
    for archive_id, archive in self._data_archives.items():
        destinies: Optional[
            Mapping[str, uuid.UUID]
        ] = archive.find_destinies_for_value(
            value_id=value_id, alias_filter=alias_filter
        )
        if not destinies:
            continue
        for k, v in destinies.items():
            if k in all_destinies.keys():
                raise Exception(f"Duplicate destiny '{k}' for value '{value_id}'.")
            all_destinies[k] = v

    return all_destinies

find_store_id_for_value(self, value_id)

Source code in kiara/registries/data/__init__.py

def find_store_id_for_value(self, value_id: uuid.UUID) -> Optional[str]:

    if value_id in self._value_archive_lookup_map.keys():
        return self._value_archive_lookup_map[value_id]

    matches = []
    for store_id, store in self.data_archives.items():
        match = store.has_value(value_id=value_id)
        if match:
            matches.append(store_id)

    if len(matches) == 0:
        return None
    elif len(matches) > 1:
        raise Exception(
            f"Found value with id '{value_id}' in multiple archives, this is not supported (yet): {matches}"
        )

    self._value_archive_lookup_map[value_id] = matches[0]
    return matches[0]

find_values_for_hash(self, value_hash, data_type_name=None)

Source code in kiara/registries/data/__init__.py

def find_values_for_hash(
    self, value_hash: str, data_type_name: Optional[str] = None
) -> Set[Value]:

    if data_type_name:
        raise NotImplementedError()

    stored = self._values_by_hash.get(value_hash, None)
    if stored is None:
        matches: Dict[uuid.UUID, List[str]] = {}
        for store_id, store in self.data_archives.items():
            value_ids = store.find_values_with_hash(
                value_hash=value_hash, data_type_name=data_type_name
            )
            for v_id in value_ids:
                matches.setdefault(v_id, []).append(store_id)

        stored = set()
        for v_id, store_ids in matches.items():
            if len(store_ids) > 1:
                raise Exception(
                    f"Found multiple stores for value id '{v_id}', this is not supported (yet)."
                )
            self._value_archive_lookup_map[v_id] = store_ids[0]
            stored.add(v_id)

        if stored:
            self._values_by_hash[value_hash] = stored

    return set((self.get_value(value_id=v_id) for v_id in stored))

get_archive(self, archive_id=None)

Source code in kiara/registries/data/__init__.py

def get_archive(
    self, archive_id: Union[None, uuid.UUID, str] = None
) -> DataArchive:

    if archive_id is None:
        archive_id = self.default_data_store
        if archive_id is None:
            raise Exception("Can't retrieve default data archive, none set (yet).")

    if isinstance(archive_id, uuid.UUID):
        for archive in self._data_archives.values():
            if archive.archive_id == archive_id:
                return archive

        raise Exception(
            f"Can't retrieve archive with id '{archive_id}': no archive with that id registered."
        )

    if archive_id in self._data_archives.keys():
        return self._data_archives[archive_id]
    else:
        try:
            _archive_id = uuid.UUID(archive_id)
            for archive in self._data_archives.values():
                if archive.archive_id == _archive_id:
                    return archive
                raise Exception(
                    f"Can't retrieve archive with id '{archive_id}': no archive with that id registered."
                )
        except Exception:
            pass

    raise Exception(
        f"Can't retrieve archive with id '{archive_id}': no archive with that id registered."
    )

get_value(self, value_id)

Source code in kiara/registries/data/__init__.py

def get_value(self, value_id: Union[uuid.UUID, Value, str]) -> Value:
    _value_id = None
    if not isinstance(value_id, uuid.UUID):
        # fallbacks for common mistakes, this should error out if not a Value or string.
        if hasattr(value_id, "value_id"):
            _value_id: Optional[uuid.UUID] = value_id.value_id  # type: ignore
        else:

            try:
                _value_id = uuid.UUID(
                    value_id  # type: ignore
                )  # this should fail if not string or wrong string format
            except ValueError:
                _value_id = None

            if _value_id is None:

                if not isinstance(value_id, str):
                    raise Exception(
                        f"Can't retrieve value for '{value_id}': invalid type '{type(value_id)}'."
                    )

                if ":" not in value_id:
                    raise Exception(
                        f"Can't retrieve value for '{value_id}': can't determine reference type."
                    )

                ref_type, rest = value_id.split(":", maxsplit=1)

                if ref_type == "value":
                    _value_id = uuid.UUID(rest)
                elif ref_type == "alias":
                    _value_id = self._kiara.alias_registry.find_value_id_for_alias(
                        alias=rest
                    )
                    if _value_id is None:
                        raise NoSuchValueAliasException(
                            alias=rest,
                            msg=f"Can't retrive value for alias '{rest}': no such alias registered.",
                        )
                else:
                    raise Exception(
                        f"Can't retrieve value for '{value_id}': invalid reference type '{ref_type}'."
                    )
    else:
        _value_id = value_id

    assert _value_id is not None

    if _value_id in self._registered_values.keys():
        value = self._registered_values[_value_id]
        return value

    matches = []
    for store_id, store in self.data_archives.items():
        match = store.has_value(value_id=_value_id)
        if match:
            matches.append(store_id)

    if len(matches) == 0:
        raise NoSuchValueIdException(
            value_id=_value_id, msg=f"No value registered with id: {value_id}"
        )
    elif len(matches) > 1:
        raise NoSuchValueIdException(
            value_id=_value_id,
            msg=f"Found value with id '{value_id}' in multiple archives, this is not supported (yet): {matches}",
        )

    self._value_archive_lookup_map[_value_id] = matches[0]
    stored_value = self.get_archive(matches[0]).retrieve_value(value_id=_value_id)
    stored_value._set_registry(self)
    stored_value._is_stored = True

    self._registered_values[_value_id] = stored_value
    return self._registered_values[_value_id]

load_data(self, values)

Source code in kiara/registries/data/__init__.py

def load_data(self, values: Mapping[str, Optional[uuid.UUID]]) -> Mapping[str, Any]:

    result_values = self.load_values(values=values)
    return {k: v.data for k, v in result_values.items()}

load_values(self, values)

Source code in kiara/registries/data/__init__.py

def load_values(self, values: Mapping[str, Optional[uuid.UUID]]) -> ValueMap:

    value_items = {}
    schemas = {}
    for field_name, value_id in values.items():
        if value_id is None:
            value_id = NONE_VALUE_ID

        value = self.get_value(value_id=value_id)
        value_items[field_name] = value
        schemas[field_name] = value.value_schema

    return ValueMapReadOnly(value_items=value_items, values_schema=schemas)

pretty_print_data(self, value_id, target_type='terminal_renderable', **render_config)

Source code in kiara/registries/data/__init__.py

def pretty_print_data(
    self,
    value_id: uuid.UUID,
    target_type="terminal_renderable",
    **render_config: Any,
) -> Any:

    assert isinstance(value_id, uuid.UUID)

    return pretty_print_data(
        kiara=self._kiara,
        value_id=value_id,
        target_type=target_type,
        **render_config,
    )

register_data(self, data, schema=None, pedigree=None, pedigree_output_name=None, reuse_existing=True)

Source code in kiara/registries/data/__init__.py

def register_data(
    self,
    data: Any,
    schema: Union[ValueSchema, str] = None,
    pedigree: Optional[ValuePedigree] = None,
    pedigree_output_name: str = None,
    reuse_existing: bool = True,
) -> Value:

    value, newly_created = self._create_value(
        data=data,
        schema=schema,
        pedigree=pedigree,
        pedigree_output_name=pedigree_output_name,
        reuse_existing=reuse_existing,
    )

    if newly_created:
        self._values_by_hash.setdefault(value.value_hash, set()).add(value.value_id)
        self._registered_values[value.value_id] = value
        self._cached_data[value.value_id] = data

        event = ValueRegisteredEvent(kiara_id=self._kiara.id, value=value)
        self._event_callback(event)

    return value

register_data_archive(self, archive, alias=None, set_as_default_store=None)

Source code in kiara/registries/data/__init__.py

def register_data_archive(
    self,
    archive: DataArchive,
    alias: str = None,
    set_as_default_store: Optional[bool] = None,
):

    data_store_id = archive.register_archive(kiara=self._kiara)
    if alias is None:
        alias = str(data_store_id)

    if alias in self._data_archives.keys():
        raise Exception(f"Can't add store, alias '{alias}' already registered.")
    self._data_archives[alias] = archive
    is_store = False
    is_default_store = False
    if isinstance(archive, DataStore):
        is_store = True

        if set_as_default_store and self._default_data_store is not None:
            raise Exception(
                f"Can't set data store '{alias}' as default store: default store already set."
            )

        if self._default_data_store is None or set_as_default_store:
            is_default_store = True
            self._default_data_store = alias

    event = DataArchiveAddedEvent.construct(
        kiara_id=self._kiara.id,
        data_archive_id=archive.archive_id,
        data_archive_alias=alias,
        is_store=is_store,
        is_default_store=is_default_store,
    )
    self._event_callback(event)

retrieve_all_available_value_ids(self)

Source code in kiara/registries/data/__init__.py

def retrieve_all_available_value_ids(self) -> Set[uuid.UUID]:

    result: Set[uuid.UUID] = set()
    for store in self._data_archives.values():
        ids = store.value_ids
        result.update(ids)

    return result

retrieve_chunk(self, chunk_id, archive_id=None, as_file=None, symlink_ok=True)

Source code in kiara/registries/data/__init__.py

def retrieve_chunk(
    self,
    chunk_id: str,
    archive_id: Optional[uuid.UUID] = None,
    as_file: Union[None, bool, str] = None,
    symlink_ok: bool = True,
) -> Union[str, bytes]:

    if archive_id is None:
        raise NotImplementedError()

    archive = self.get_archive(archive_id)
    chunk = archive.retrieve_chunk(chunk_id, as_file=as_file, symlink_ok=symlink_ok)

    return chunk

retrieve_persisted_value_details(self, value_id)

Source code in kiara/registries/data/__init__.py

def retrieve_persisted_value_details(self, value_id: uuid.UUID) -> PersistedData:

    if (
        value_id in self._persisted_value_descs.keys()
        and self._persisted_value_descs[value_id] is not None
    ):
        persisted_details = self._persisted_value_descs[value_id]
        assert persisted_details is not None
    else:
        # now, the value_store map should contain this value_id
        store_id = self.find_store_id_for_value(value_id=value_id)
        if store_id is None:
            raise Exception(
                f"Can't find store for persisted data of value: {value_id}"
            )

        store = self.get_archive(store_id)
        assert value_id in self._registered_values.keys()
        # self.get_value(value_id=value_id)
        persisted_details = store.retrieve_serialized_value(value=value_id)
        for c in persisted_details.chunk_id_map.values():
            c._data_registry = self._kiara.data_registry
        self._persisted_value_descs[value_id] = persisted_details

    return persisted_details

retrieve_serialized_value(self, value_id)

Create a LoadConfig object from the details of the persisted version of this value.

Source code in kiara/registries/data/__init__.py

def retrieve_serialized_value(
    self, value_id: uuid.UUID
) -> Optional[SerializedData]:
    """Create a LoadConfig object from the details of the persisted version of this value."""

    pv = self.retrieve_persisted_value_details(value_id=value_id)
    if pv is None:
        return None

    return pv

retrieve_value_data(self, value, target_profile=None)

Source code in kiara/registries/data/__init__.py

def retrieve_value_data(
    self, value: Union[uuid.UUID, Value], target_profile: Optional[str] = None
) -> Any:

    if isinstance(value, uuid.UUID):
        value = self.get_value(value_id=value)

    if value.value_id in self._cached_data.keys():
        return self._cached_data[value.value_id]

    if value._serialized_data is None:
        serialized_data: Union[
            str, SerializedData
        ] = self.retrieve_persisted_value_details(value_id=value.value_id)
        value._serialized_data = serialized_data
    else:
        serialized_data = value._serialized_data

    if isinstance(serialized_data, str):
        raise Exception(
            f"Can't retrieve serialized version of value '{value.value_id}', this is most likely a bug."
        )

    manifest = serialized_data.metadata.deserialize.get("python_object", None)
    if manifest is None:
        raise Exception(
            f"No deserialize operation found for data type: {value.data_type_name}"
        )

    module = self._kiara.create_module(manifest=manifest)
    op = Operation.create_from_module(module=module)

    input_field_match: Optional[str] = None
    if len(op.inputs_schema) == 1:
        input_field_match = next(iter(op.inputs_schema.keys()))
    else:
        for input_field, schema in op.inputs_schema.items():
            if schema.type == value.data_type_name:
                if input_field_match is not None:
                    raise Exception(
                        f"Can't determine input field for deserialization operation '{module.module_type_name}': multiple input fields with type '{input_field_match}'."
                    )
                else:
                    input_field_match = input_field
    if input_field_match is None:
        raise Exception(
            f"Can't determine input field for deserialization operation '{module.module_type_name}'."
        )

    result_field_match: Optional[str] = None
    for result_field, schema in op.outputs_schema.items():
        if schema.type == "python_object":
            if result_field_match is not None:
                raise Exception(
                    f"Can't determine result field for deserialization operation '{module.module_type_name}': multiple result fields with type 'python_object'."
                )
            else:
                result_field_match = result_field
    if result_field_match is None:
        raise Exception(
            f"Can't determine result field for deserialization operation '{module.module_type_name}'."
        )

    inputs = {input_field_match: value}

    result = op.run(kiara=self._kiara, inputs=inputs)
    python_object = result.get_value_data(result_field_match)
    self._cached_data[value.value_id] = python_object

    return python_object

    # op_type: DeSerializeOperationType = self._kiara.operation_registry.get_operation_type("deserialize")  # type: ignore
    # ops = op_type.find_deserialzation_operation_for_type_and_profile(
    #     serialized_data.data_type, serialized_data.serialization_profile
    # )
    #
    # if len(ops) > 1:
    #     raise Exception("No unique op.")
    #
    # if not ops:
    #     raise Exception(
    #         f"No deserialize operation found for data type: {value.data_type_name}"
    #     )
    #
    # op = ops[0]
    # inputs = {"value": serialized_data}
    #
    # result = op.run(kiara=self._kiara, inputs=inputs)
    #
    # python_object = result.get_value_data("python_object")
    # self._cached_data[value.value_id] = python_object
    #
    # return python_object

store_value(self, value, store_id=None)

Source code in kiara/registries/data/__init__.py

def store_value(
    self,
    value: Union[Value, uuid.UUID],
    store_id: Optional[str] = None,
) -> Optional[PersistedData]:

    if store_id is None:
        store_id = self.default_data_store

    if isinstance(value, uuid.UUID):
        value = self.get_value(value)

    store: DataStore = self.get_archive(archive_id=store_id)  # type: ignore
    if not isinstance(store, DataStore):
        raise Exception(f"Can't store value into store '{store_id}': not writable.")

    # make sure all property values are available
    if value.pedigree != ORPHAN:
        for value_id in value.pedigree.inputs.values():
            self.store_value(value=value_id, store_id=store_id)

    if not store.has_value(value.value_id):
        event = ValuePreStoreEvent.construct(kiara_id=self._kiara.id, value=value)
        self._event_callback(event)
        persisted_value = store.store_value(value)
        value._is_stored = True
        self._value_archive_lookup_map[value.value_id] = store_id
        self._persisted_value_descs[value.value_id] = persisted_value
        property_values = value.property_values

        for property, property_value in property_values.items():
            self.store_value(value=property_value, store_id=store_id)
    else:
        persisted_value = None

    store_event = ValueStoredEvent.construct(kiara_id=self._kiara.id, value=value)
    self._event_callback(store_event)

    return persisted_value

Modules

data_store special

logger

Classes

BaseDataStore (DataStore)

Source code in kiara/registries/data/data_store/__init__.py

class BaseDataStore(DataStore):
    # @abc.abstractmethod
    # def _persist_bytes(self, bytes_structure: BytesStructure) -> BytesAliasStructure:
    #     pass

    @abc.abstractmethod
    def _persist_stored_value_info(self, value: Value, persisted_value: PersistedData):
        pass

    @abc.abstractmethod
    def _persist_value_details(self, value: Value):
        pass

    @abc.abstractmethod
    def _persist_value_data(self, value: Value) -> PersistedData:
        pass

    @abc.abstractmethod
    def _persist_value_pedigree(self, value: Value):
        """Create an internal link from a value to its pedigree (and pedigree details).

        This is so that the 'retrieve_job_record' can be used to prevent running the same job again, and the link of value
        to the job that produced it is preserved.
        """

    @abc.abstractmethod
    def _persist_environment_details(
        self, env_type: str, env_hash: str, env_data: Mapping[str, Any]
    ):
        pass

    @abc.abstractmethod
    def _persist_destiny_backlinks(self, value: Value):
        pass

    def store_value(self, value: Value) -> PersistedData:

        logger.debug(
            "store.value",
            data_type=value.value_schema.type,
            value_id=value.value_id,
            value_hash=value.value_hash,
        )

        # first, persist environment information
        for env_type, env_hash in value.pedigree.environments.items():
            cached = self._env_cache.get(env_type, {}).get(env_hash, None)
            if cached is not None:
                continue

            env = self.kiara_context.environment_registry.get_environment_for_cid(
                env_hash
            )
            self.persist_environment(env)

        # save the value data and metadata
        persisted_value = self._persist_value(value)
        self._persisted_value_cache[value.value_id] = persisted_value
        self._value_cache[value.value_id] = value
        self._value_hash_index.setdefault(value.value_hash, set()).add(value.value_id)

        # now link the output values to the manifest
        # then, make sure the manifest is persisted
        self._persist_value_pedigree(value=value)

        return persisted_value

    def _persist_value(self, value: Value) -> PersistedData:

        # TODO: check if value id is already persisted?
        persisted_value_info: PersistedData = self._persist_value_data(value=value)
        if not persisted_value_info:
            raise Exception(
                "Can't write persisted value info, no load config returned when persisting value."
            )
        if not isinstance(persisted_value_info, PersistedData):
            raise Exception(
                f"Can't write persisted value info, invalid result type '{type(persisted_value_info)}' when persisting value."
            )

        self._persist_stored_value_info(
            value=value, persisted_value=persisted_value_info
        )
        self._persist_value_details(value=value)
        if value.destiny_backlinks:
            self._persist_destiny_backlinks(value=value)

        return persisted_value_info

    def persist_environment(self, environment: RuntimeEnvironment):
        """Persist the specified environment.

        The environment is stored as a dictionary, including it's schema, not as the actual Python model.
        This is to make sure it can still be loaded later on, in case the Python model has changed in later versions.
        """

        env_type = environment.get_environment_type_name()
        env_hash = str(environment.instance_cid)

        env = self._env_cache.get(env_type, {}).get(env_hash, None)
        if env is not None:
            return

        env_data = environment.as_dict_with_schema()
        self._persist_environment_details(
            env_type=env_type, env_hash=env_hash, env_data=env_data
        )
        self._env_cache.setdefault(env_type, {})[env_hash] = env_data

    def create_renderable(self, **config: Any) -> RenderableType:
        """Create a renderable for this module configuration."""

        from kiara.utils.output import create_renderable_from_values

        all_values = {}
        for value_id in self.value_ids:

            value = self.kiara_context.data_registry.get_value(value_id)
            all_values[str(value_id)] = value
        table = create_renderable_from_values(values=all_values, config=config)

        return table

Methods

create_renderable(self, **config)

Create a renderable for this module configuration.

Source code in kiara/registries/data/data_store/__init__.py

def create_renderable(self, **config: Any) -> RenderableType:
    """Create a renderable for this module configuration."""

    from kiara.utils.output import create_renderable_from_values

    all_values = {}
    for value_id in self.value_ids:

        value = self.kiara_context.data_registry.get_value(value_id)
        all_values[str(value_id)] = value
    table = create_renderable_from_values(values=all_values, config=config)

    return table

persist_environment(self, environment)

Persist the specified environment.

The environment is stored as a dictionary, including it's schema, not as the actual Python model. This is to make sure it can still be loaded later on, in case the Python model has changed in later versions.

Source code in kiara/registries/data/data_store/__init__.py

def persist_environment(self, environment: RuntimeEnvironment):
    """Persist the specified environment.

    The environment is stored as a dictionary, including it's schema, not as the actual Python model.
    This is to make sure it can still be loaded later on, in case the Python model has changed in later versions.
    """

    env_type = environment.get_environment_type_name()
    env_hash = str(environment.instance_cid)

    env = self._env_cache.get(env_type, {}).get(env_hash, None)
    if env is not None:
        return

    env_data = environment.as_dict_with_schema()
    self._persist_environment_details(
        env_type=env_type, env_hash=env_hash, env_data=env_data
    )
    self._env_cache.setdefault(env_type, {})[env_hash] = env_data

store_value(self, value)

"Store the value, its data and metadata into the store.

Parameters:

Name	Type	Description	Default
value	Value	the value to persist	required

Returns:

Type	Description
PersistedData	the load config that is needed to retrieve the value data later

Source code in kiara/registries/data/data_store/__init__.py

def store_value(self, value: Value) -> PersistedData:

    logger.debug(
        "store.value",
        data_type=value.value_schema.type,
        value_id=value.value_id,
        value_hash=value.value_hash,
    )

    # first, persist environment information
    for env_type, env_hash in value.pedigree.environments.items():
        cached = self._env_cache.get(env_type, {}).get(env_hash, None)
        if cached is not None:
            continue

        env = self.kiara_context.environment_registry.get_environment_for_cid(
            env_hash
        )
        self.persist_environment(env)

    # save the value data and metadata
    persisted_value = self._persist_value(value)
    self._persisted_value_cache[value.value_id] = persisted_value
    self._value_cache[value.value_id] = value
    self._value_hash_index.setdefault(value.value_hash, set()).add(value.value_id)

    # now link the output values to the manifest
    # then, make sure the manifest is persisted
    self._persist_value_pedigree(value=value)

    return persisted_value

DataArchive (BaseArchive)

Source code in kiara/registries/data/data_store/__init__.py

class DataArchive(BaseArchive):
    @classmethod
    def supported_item_types(cls) -> Iterable[str]:

        return ["data"]

    def __init__(self, archive_id: uuid.UUID, config: ARCHIVE_CONFIG_CLS):

        super().__init__(archive_id=archive_id, config=config)

        self._env_cache: Dict[str, Dict[str, Mapping[str, Any]]] = {}
        self._value_cache: Dict[uuid.UUID, Value] = {}
        self._persisted_value_cache: Dict[uuid.UUID, PersistedData] = {}
        self._value_hash_index: Dict[str, Set[uuid.UUID]] = {}

    def retrieve_serialized_value(
        self, value: Union[uuid.UUID, Value]
    ) -> PersistedData:

        if isinstance(value, Value):
            value_id: uuid.UUID = value.value_id
            _value: Optional[Value] = value
        else:
            value_id = value
            _value = None

        if value_id in self._persisted_value_cache.keys():
            return self._persisted_value_cache[value_id]

        if _value is None:
            _value = self.retrieve_value(value_id)

        assert _value is not None

        persisted_value = self._retrieve_serialized_value(value=_value)
        self._persisted_value_cache[_value.value_id] = persisted_value
        return persisted_value

    @abc.abstractmethod
    def _retrieve_serialized_value(self, value: Value) -> PersistedData:
        pass

    def retrieve_value(self, value_id: uuid.UUID) -> Value:

        cached = self._value_cache.get(value_id, None)
        if cached is not None:
            return cached

        value_data = self._retrieve_value_details(value_id=value_id)

        value_schema = ValueSchema(**value_data["value_schema"])
        # data_type = self._kiara.get_value_type(
        #         data_type=value_schema.type, data_type_config=value_schema.type_config
        #     )

        pedigree = ValuePedigree(**value_data["pedigree"])
        value = Value(
            value_id=value_data["value_id"],
            kiara_id=self.kiara_context.id,
            value_schema=value_schema,
            value_status=value_data["value_status"],
            value_size=value_data["value_size"],
            value_hash=value_data["value_hash"],
            pedigree=pedigree,
            pedigree_output_name=value_data["pedigree_output_name"],
            data_type_class=value_data["data_type_class"],
            property_links=value_data["property_links"],
            destiny_backlinks=value_data["destiny_backlinks"],
        )

        self._value_cache[value_id] = value
        return self._value_cache[value_id]

    @abc.abstractmethod
    def _retrieve_value_details(self, value_id: uuid.UUID) -> Mapping[str, Any]:
        pass

    @property
    def value_ids(self) -> Iterable[uuid.UUID]:
        return self._retrieve_all_value_ids()

    @abc.abstractmethod
    def _retrieve_all_value_ids(
        self, data_type_name: Optional[str] = None
    ) -> Iterable[uuid.UUID]:
        pass

    def has_value(self, value_id: uuid.UUID) -> bool:
        """Check whether the specific value_id is persisted in this data store.

        Implementing classes are encouraged to override this method, and choose a suitable, implementation specific
        way to quickly determine whether a value id is valid for this data store.

        Arguments:
            value_id: the id of the value to check.
        Returns:
            whether this data store contains the value with the specified id
        """

        return value_id in self._retrieve_all_value_ids()

    def retrieve_environment_details(
        self, env_type: str, env_hash: str
    ) -> Mapping[str, Any]:
        """Retrieve the environment details with the specified type and hash.

        The environment is stored by the data store as a dictionary, including it's schema, not as the actual Python model.
        This is to make sure it can still be loaded later on, in case the Python model has changed in later versions.
        """

        cached = self._env_cache.get(env_type, {}).get(env_hash, None)
        if cached is not None:
            return cached

        env = self._retrieve_environment_details(env_type=env_type, env_hash=env_hash)
        self._env_cache.setdefault(env_type, {})[env_hash] = env
        return env

    @abc.abstractmethod
    def _retrieve_environment_details(
        self, env_type: str, env_hash: str
    ) -> Mapping[str, Any]:
        pass

    def find_values_with_hash(
        self,
        value_hash: str,
        value_size: Optional[int] = None,
        data_type_name: Optional[str] = None,
    ) -> Set[uuid.UUID]:

        if data_type_name is not None:
            raise NotImplementedError()

        if value_size is not None:
            raise NotImplementedError()

        if value_hash in self._value_hash_index.keys():
            value_ids: Optional[Set[uuid.UUID]] = self._value_hash_index[value_hash]
        else:
            value_ids = self._find_values_with_hash(
                value_hash=value_hash, data_type_name=data_type_name
            )
            if value_ids is None:
                value_ids = set()
            self._value_hash_index[value_hash] = value_ids

        assert value_ids is not None
        return value_ids

    @abc.abstractmethod
    def _find_values_with_hash(
        self,
        value_hash: str,
        value_size: Optional[int] = None,
        data_type_name: Optional[str] = None,
    ) -> Optional[Set[uuid.UUID]]:
        pass

    def find_destinies_for_value(
        self, value_id: uuid.UUID, alias_filter: Optional[str] = None
    ) -> Optional[Mapping[str, uuid.UUID]]:

        return self._find_destinies_for_value(
            value_id=value_id, alias_filter=alias_filter
        )

    @abc.abstractmethod
    def _find_destinies_for_value(
        self, value_id: uuid.UUID, alias_filter: Optional[str] = None
    ) -> Optional[Mapping[str, uuid.UUID]]:
        pass

    @abc.abstractmethod
    def retrieve_chunk(
        self,
        chunk_id: str,
        as_file: Union[bool, str, None] = None,
        symlink_ok: bool = True,
    ) -> Union[bytes, str]:
        pass

    # def retrieve_job_record(self, inputs_manifest: InputsManifest) -> Optional[JobRecord]:
    #     return self._retrieve_job_record(
    #         manifest_hash=inputs_manifest.manifest_hash, jobs_hash=inputs_manifest.jobs_hash
    #     )
    #
    # @abc.abstractmethod
    # def _retrieve_job_record(
    #     self, manifest_hash: int, jobs_hash: int
    # ) -> Optional[JobRecord]:
    #     pass

value_ids: Iterable[uuid.UUID] property readonly

Methods

find_destinies_for_value(self, value_id, alias_filter=None)

Source code in kiara/registries/data/data_store/__init__.py

def find_destinies_for_value(
    self, value_id: uuid.UUID, alias_filter: Optional[str] = None
) -> Optional[Mapping[str, uuid.UUID]]:

    return self._find_destinies_for_value(
        value_id=value_id, alias_filter=alias_filter
    )

find_values_with_hash(self, value_hash, value_size=None, data_type_name=None)

Source code in kiara/registries/data/data_store/__init__.py

def find_values_with_hash(
    self,
    value_hash: str,
    value_size: Optional[int] = None,
    data_type_name: Optional[str] = None,
) -> Set[uuid.UUID]:

    if data_type_name is not None:
        raise NotImplementedError()

    if value_size is not None:
        raise NotImplementedError()

    if value_hash in self._value_hash_index.keys():
        value_ids: Optional[Set[uuid.UUID]] = self._value_hash_index[value_hash]
    else:
        value_ids = self._find_values_with_hash(
            value_hash=value_hash, data_type_name=data_type_name
        )
        if value_ids is None:
            value_ids = set()
        self._value_hash_index[value_hash] = value_ids

    assert value_ids is not None
    return value_ids

has_value(self, value_id)

Check whether the specific value_id is persisted in this data store.

Implementing classes are encouraged to override this method, and choose a suitable, implementation specific way to quickly determine whether a value id is valid for this data store.

Parameters:

Name	Type	Description	Default
value_id	UUID	the id of the value to check.	required

Returns:

Type	Description
bool	whether this data store contains the value with the specified id

Source code in kiara/registries/data/data_store/__init__.py

def has_value(self, value_id: uuid.UUID) -> bool:
    """Check whether the specific value_id is persisted in this data store.

    Implementing classes are encouraged to override this method, and choose a suitable, implementation specific
    way to quickly determine whether a value id is valid for this data store.

    Arguments:
        value_id: the id of the value to check.
    Returns:
        whether this data store contains the value with the specified id
    """

    return value_id in self._retrieve_all_value_ids()

retrieve_chunk(self, chunk_id, as_file=None, symlink_ok=True)

Source code in kiara/registries/data/data_store/__init__.py

@abc.abstractmethod
def retrieve_chunk(
    self,
    chunk_id: str,
    as_file: Union[bool, str, None] = None,
    symlink_ok: bool = True,
) -> Union[bytes, str]:
    pass

retrieve_environment_details(self, env_type, env_hash)

Retrieve the environment details with the specified type and hash.

The environment is stored by the data store as a dictionary, including it's schema, not as the actual Python model. This is to make sure it can still be loaded later on, in case the Python model has changed in later versions.

Source code in kiara/registries/data/data_store/__init__.py

def retrieve_environment_details(
    self, env_type: str, env_hash: str
) -> Mapping[str, Any]:
    """Retrieve the environment details with the specified type and hash.

    The environment is stored by the data store as a dictionary, including it's schema, not as the actual Python model.
    This is to make sure it can still be loaded later on, in case the Python model has changed in later versions.
    """

    cached = self._env_cache.get(env_type, {}).get(env_hash, None)
    if cached is not None:
        return cached

    env = self._retrieve_environment_details(env_type=env_type, env_hash=env_hash)
    self._env_cache.setdefault(env_type, {})[env_hash] = env
    return env

retrieve_serialized_value(self, value)

Source code in kiara/registries/data/data_store/__init__.py

def retrieve_serialized_value(
    self, value: Union[uuid.UUID, Value]
) -> PersistedData:

    if isinstance(value, Value):
        value_id: uuid.UUID = value.value_id
        _value: Optional[Value] = value
    else:
        value_id = value
        _value = None

    if value_id in self._persisted_value_cache.keys():
        return self._persisted_value_cache[value_id]

    if _value is None:
        _value = self.retrieve_value(value_id)

    assert _value is not None

    persisted_value = self._retrieve_serialized_value(value=_value)
    self._persisted_value_cache[_value.value_id] = persisted_value
    return persisted_value

retrieve_value(self, value_id)

Source code in kiara/registries/data/data_store/__init__.py

def retrieve_value(self, value_id: uuid.UUID) -> Value:

    cached = self._value_cache.get(value_id, None)
    if cached is not None:
        return cached

    value_data = self._retrieve_value_details(value_id=value_id)

    value_schema = ValueSchema(**value_data["value_schema"])
    # data_type = self._kiara.get_value_type(
    #         data_type=value_schema.type, data_type_config=value_schema.type_config
    #     )

    pedigree = ValuePedigree(**value_data["pedigree"])
    value = Value(
        value_id=value_data["value_id"],
        kiara_id=self.kiara_context.id,
        value_schema=value_schema,
        value_status=value_data["value_status"],
        value_size=value_data["value_size"],
        value_hash=value_data["value_hash"],
        pedigree=pedigree,
        pedigree_output_name=value_data["pedigree_output_name"],
        data_type_class=value_data["data_type_class"],
        property_links=value_data["property_links"],
        destiny_backlinks=value_data["destiny_backlinks"],
    )

    self._value_cache[value_id] = value
    return self._value_cache[value_id]

supported_item_types() classmethod

Source code in kiara/registries/data/data_store/__init__.py

@classmethod
def supported_item_types(cls) -> Iterable[str]:

    return ["data"]

DataStore (DataArchive)

Source code in kiara/registries/data/data_store/__init__.py

class DataStore(DataArchive):
    @classmethod
    def is_writeable(cls) -> bool:
        return True

    @abc.abstractmethod
    def store_value(self, value: Value) -> PersistedData:
        """ "Store the value, its data and metadata into the store.

        Arguments:
            value: the value to persist

        Returns:
            the load config that is needed to retrieve the value data later
        """

Methods

is_writeable() classmethod

Source code in kiara/registries/data/data_store/__init__.py

@classmethod
def is_writeable(cls) -> bool:
    return True

store_value(self, value)

"Store the value, its data and metadata into the store.

Parameters:

Name	Type	Description	Default
value	Value	the value to persist	required

Returns:

Type	Description
PersistedData	the load config that is needed to retrieve the value data later

Source code in kiara/registries/data/data_store/__init__.py

@abc.abstractmethod
def store_value(self, value: Value) -> PersistedData:
    """ "Store the value, its data and metadata into the store.

    Arguments:
        value: the value to persist

    Returns:
        the load config that is needed to retrieve the value data later
    """

Modules

filesystem_store

DEFAULT_HASHFS_DEPTH

DEFAULT_HASHFS_WIDTH

DEFAULT_HASH_FS_ALGORITHM

VALUE_DETAILS_FILE_NAME

logger

Classes

EntityType (Enum)

An enumeration.

Source code in kiara/registries/data/data_store/filesystem_store.py

class EntityType(Enum):

    VALUE = "values"
    VALUE_DATA = "value_data"
    ENVIRONMENT = "environments"
    MANIFEST = "manifests"
    DESTINY_LINK = "destiny_links"

DESTINY_LINK

ENVIRONMENT

MANIFEST

VALUE

VALUE_DATA

FileSystemDataArchive (DataArchive, JobArchive)

Data store that loads data from the local filesystem.

Source code in kiara/registries/data/data_store/filesystem_store.py

class FileSystemDataArchive(DataArchive, JobArchive):
    """Data store that loads data from the local filesystem."""

    _archive_type_name = "filesystem_data_archive"
    _config_cls = FileSystemArchiveConfig

    # @classmethod
    # def supported_item_types(cls) -> Iterable[str]:
    #
    #     return ["data", "job_record"]

    @classmethod
    def is_writeable(cls) -> bool:
        return False

    def __init__(self, archive_id: uuid.UUID, config: FileSystemArchiveConfig):

        DataArchive.__init__(self, archive_id=archive_id, config=config)
        self._base_path: Optional[Path] = None
        self._hashfs_path: Optional[Path] = None
        self._hashfs: Optional[HashFS] = None

    # def get_job_archive_id(self) -> uuid.UUID:
    #     return self._kiara.id

    @property
    def data_store_path(self) -> Path:

        if self._base_path is not None:
            return self._base_path

        self._base_path = Path(self.config.archive_path).absolute()
        self._base_path.mkdir(parents=True, exist_ok=True)
        return self._base_path

    @property
    def hash_fs_path(self) -> Path:

        if self._hashfs_path is None:
            self._hashfs_path = self.data_store_path / "hash_fs"
        return self._hashfs_path

    @property
    def hashfs(self) -> HashFS:

        if self._hashfs is None:
            self._hashfs = HashFS(
                self.hash_fs_path.as_posix(),
                depth=DEFAULT_HASHFS_DEPTH,
                width=DEFAULT_HASHFS_WIDTH,
                algorithm=DEFAULT_HASH_FS_ALGORITHM,
            )
        return self._hashfs

    def get_path(
        self, entity_type: Optional[EntityType] = None, base_path: Optional[Path] = None
    ) -> Path:
        if base_path is None:
            if entity_type is None:
                result = self.data_store_path
            else:
                result = self.data_store_path / entity_type.value
        else:
            if entity_type is None:
                result = base_path
            else:
                result = base_path / entity_type.value

        result.mkdir(parents=True, exist_ok=True)
        return result

    def _retrieve_environment_details(
        self, env_type: str, env_hash: str
    ) -> Mapping[str, Any]:

        base_path = self.get_path(entity_type=EntityType.ENVIRONMENT)
        env_details_file = base_path / f"{env_type}_{env_hash}.json"

        if not env_details_file.exists():
            raise Exception(
                f"Can't load environment details, file does not exist: {env_details_file.as_posix()}"
            )

        environment = orjson.loads(env_details_file.read_text())
        return environment

    def find_matching_job_record(
        self, inputs_manifest: InputsManifest
    ) -> Optional[JobRecord]:

        return self._retrieve_job_record(
            manifest_hash=str(inputs_manifest.instance_cid),
            jobs_hash=inputs_manifest.job_hash,
        )

    def _retrieve_job_record(
        self, manifest_hash: str, jobs_hash: str
    ) -> Optional[JobRecord]:

        base_path = self.get_path(entity_type=EntityType.MANIFEST)
        manifest_folder = base_path / str(manifest_hash)

        if not manifest_folder.exists():
            return None

        manifest_file = manifest_folder / "manifest.json"

        if not manifest_file.exists():
            raise Exception(
                f"No 'manifests.json' file for manifest with hash: {manifest_hash}"
            )

        manifest_data = orjson.loads(manifest_file.read_text())

        job_folder = manifest_folder / jobs_hash

        if not job_folder.exists():
            return None

        inputs_file_name = job_folder / "inputs.json"
        if not inputs_file_name.exists():
            raise Exception(
                f"No 'inputs.json' file for manifest/inputs hash-combo: {manifest_hash} / {jobs_hash}"
            )

        inputs_data = {
            k: uuid.UUID(v)
            for k, v in orjson.loads(inputs_file_name.read_text()).items()
        }

        outputs = {}
        for output_file in job_folder.glob("output__*.json"):
            full_output_name = output_file.name[8:]
            start_value_id = full_output_name.find("__value_id__")
            output_name = full_output_name[0:start_value_id]
            value_id_str = full_output_name[start_value_id + 12 : -5]  # noqa

            value_id = uuid.UUID(value_id_str)
            outputs[output_name] = value_id

        job_id = ID_REGISTRY.generate(obj_type=JobRecord, desc="fake job id")
        job_record = JobRecord(
            job_id=job_id,
            module_type=manifest_data["module_type"],
            module_config=manifest_data["module_config"],
            inputs=inputs_data,
            outputs=outputs,
        )
        return job_record

    def _find_values_with_hash(
        self,
        value_hash: str,
        value_size: Optional[int] = None,
        data_type_name: Optional[str] = None,
    ) -> Set[uuid.UUID]:

        value_data_folder = self.get_path(entity_type=EntityType.VALUE_DATA)

        glob = f"*/{value_hash}/value_id__*.json"

        matches = list(value_data_folder.glob(glob))

        result = set()
        for match in matches:
            if not match.is_symlink():
                log_message(
                    f"Ignoring value_id file, not a symlink: {match.as_posix()}"
                )
                continue

            uuid_str = match.name[10:-5]
            value_id = uuid.UUID(uuid_str)
            result.add(value_id)

        return result

    def _find_destinies_for_value(
        self, value_id: uuid.UUID, alias_filter: Optional[str] = None
    ) -> Optional[Mapping[str, uuid.UUID]]:

        destiny_dir = self.get_path(entity_type=EntityType.DESTINY_LINK)
        destiny_value_dir = destiny_dir / str(value_id)

        if not destiny_value_dir.exists():
            return None

        destinies = {}
        for alias_link in destiny_value_dir.glob("*.json"):
            assert alias_link.is_symlink()

            alias = alias_link.name[0:-5]
            resolved = alias_link.resolve()

            value_id_str = resolved.parent.name
            value_id = uuid.UUID(value_id_str)
            destinies[alias] = value_id

        return destinies

    def _retrieve_all_value_ids(
        self, data_type_name: Optional[str] = None
    ) -> Iterable[uuid.UUID]:

        if data_type_name is not None:
            raise NotImplementedError()

        childs = self.get_path(entity_type=EntityType.VALUE).glob("*")
        folders = [uuid.UUID(x.name) for x in childs if x.is_dir()]
        return folders

    def has_value(self, value_id: uuid.UUID) -> bool:
        """Check whether the specific value_id is persisted in this data store.
        way to quickly determine whether a value id is valid for this data store.

        Arguments:
            value_id: the id of the value to check.
        Returns:
            whether this data store contains the value with the specified id
        """

        base_path = (
            self.get_path(entity_type=EntityType.VALUE)
            / str(value_id)
            / VALUE_DETAILS_FILE_NAME
        )
        return base_path.is_file()

    def _retrieve_value_details(self, value_id: uuid.UUID) -> Mapping[str, Any]:

        base_path = (
            self.get_path(entity_type=EntityType.VALUE)
            / str(value_id)
            / VALUE_DETAILS_FILE_NAME
        )
        if not base_path.is_file():
            raise Exception(
                f"Can't retrieve details for value with id '{value_id}': no value with that id stored."
            )

        value_data = orjson.loads(base_path.read_text())
        return value_data

    def _retrieve_serialized_value(self, value: Value) -> PersistedData:

        base_path = self.get_path(entity_type=EntityType.VALUE_DATA)
        data_dir = base_path / value.data_type_name / str(value.value_hash)

        serialized_value_file = data_dir / ".serialized_value.json"
        data = orjson.loads(serialized_value_file.read_text())

        return PersistedData(**data)

    def retrieve_chunk(
        self,
        chunk_id: str,
        as_file: Union[bool, str, None] = None,
        symlink_ok: bool = True,
    ) -> Union[bytes, str]:

        addr = self.hashfs.get(chunk_id)

        if as_file in (None, True):
            return addr.abspath
        elif as_file is False:
            return Path(addr.abspath).read_bytes()
        else:
            raise NotImplementedError()

data_store_path: Path property readonly

hash_fs_path: Path property readonly

hashfs: HashFS property readonly

Classes

_config_cls (ArchiveConfig) private pydantic-model

Source code in kiara/registries/data/data_store/filesystem_store.py

class FileSystemArchiveConfig(ArchiveConfig):

    archive_path: str = Field(
        description="The path where the data for this archive is stored."
    )

Attributes

archive_path: str pydantic-field required

The path where the data for this archive is stored.

Methods

find_matching_job_record(self, inputs_manifest)

Source code in kiara/registries/data/data_store/filesystem_store.py

def find_matching_job_record(
    self, inputs_manifest: InputsManifest
) -> Optional[JobRecord]:

    return self._retrieve_job_record(
        manifest_hash=str(inputs_manifest.instance_cid),
        jobs_hash=inputs_manifest.job_hash,
    )

get_path(self, entity_type=None, base_path=None)

Source code in kiara/registries/data/data_store/filesystem_store.py

def get_path(
    self, entity_type: Optional[EntityType] = None, base_path: Optional[Path] = None
) -> Path:
    if base_path is None:
        if entity_type is None:
            result = self.data_store_path
        else:
            result = self.data_store_path / entity_type.value
    else:
        if entity_type is None:
            result = base_path
        else:
            result = base_path / entity_type.value

    result.mkdir(parents=True, exist_ok=True)
    return result

has_value(self, value_id)

Check whether the specific value_id is persisted in this data store. way to quickly determine whether a value id is valid for this data store.

Parameters:

Name	Type	Description	Default
value_id	UUID	the id of the value to check.	required

Returns:

Type	Description
bool	whether this data store contains the value with the specified id

Source code in kiara/registries/data/data_store/filesystem_store.py

def has_value(self, value_id: uuid.UUID) -> bool:
    """Check whether the specific value_id is persisted in this data store.
    way to quickly determine whether a value id is valid for this data store.

    Arguments:
        value_id: the id of the value to check.
    Returns:
        whether this data store contains the value with the specified id
    """

    base_path = (
        self.get_path(entity_type=EntityType.VALUE)
        / str(value_id)
        / VALUE_DETAILS_FILE_NAME
    )
    return base_path.is_file()

is_writeable() classmethod

Source code in kiara/registries/data/data_store/filesystem_store.py

@classmethod
def is_writeable(cls) -> bool:
    return False

retrieve_chunk(self, chunk_id, as_file=None, symlink_ok=True)

Source code in kiara/registries/data/data_store/filesystem_store.py

def retrieve_chunk(
    self,
    chunk_id: str,
    as_file: Union[bool, str, None] = None,
    symlink_ok: bool = True,
) -> Union[bytes, str]:

    addr = self.hashfs.get(chunk_id)

    if as_file in (None, True):
        return addr.abspath
    elif as_file is False:
        return Path(addr.abspath).read_bytes()
    else:
        raise NotImplementedError()

FilesystemDataStore (FileSystemDataArchive, BaseDataStore)

Data store that stores data as files on the local filesystem.

Source code in kiara/registries/data/data_store/filesystem_store.py

class FilesystemDataStore(FileSystemDataArchive, BaseDataStore):
    """Data store that stores data as files on the local filesystem."""

    _archive_type_name = "filesystem_data_store"

    def _persist_environment_details(
        self, env_type: str, env_hash: str, env_data: Mapping[str, Any]
    ):

        base_path = self.get_path(entity_type=EntityType.ENVIRONMENT)
        env_details_file = base_path / f"{env_type}_{env_hash}.json"

        if not env_details_file.exists():
            env_details_file.write_text(orjson_dumps(env_data))

    def _persist_stored_value_info(self, value: Value, persisted_value: PersistedData):

        working_dir = self.get_path(entity_type=EntityType.VALUE_DATA)
        data_dir = working_dir / value.data_type_name / str(value.value_hash)
        sv_file = data_dir / ".serialized_value.json"
        data_dir.mkdir(exist_ok=True, parents=True)
        sv_file.write_text(persisted_value.json())

    def _persist_value_details(self, value: Value):

        value_dir = self.get_path(entity_type=EntityType.VALUE) / str(value.value_id)

        if value_dir.exists():
            raise Exception(
                f"Can't persist value '{value.value_id}', value directory already exists: {value_dir}"
            )
        else:
            value_dir.mkdir(parents=True, exist_ok=False)

        value_file = value_dir / VALUE_DETAILS_FILE_NAME
        value_data = value.dict()
        value_file.write_text(orjson_dumps(value_data, option=orjson.OPT_NON_STR_KEYS))

    def _persist_destiny_backlinks(self, value: Value):

        destiny_dir = self.get_path(entity_type=EntityType.DESTINY_LINK)

        for value_id, backlink in value.destiny_backlinks.items():

            destiny_value_dir = destiny_dir / str(value_id)
            destiny_value_dir.mkdir(parents=True, exist_ok=True)
            destiny_file = destiny_value_dir / f"{backlink}.json"
            assert not destiny_file.exists()

            value_dir = self.get_path(entity_type=EntityType.VALUE) / str(
                value.value_id
            )
            value_file = value_dir / VALUE_DETAILS_FILE_NAME
            assert value_file.exists()

            destiny_file.symlink_to(value_file)

    def _persist_value_data(self, value: Value) -> PersistedData:

        serialized_value: SerializedData = value.serialized_data

        chunk_id_map = {}
        for key in serialized_value.get_keys():

            data_model = serialized_value.get_serialized_data(key)

            if data_model.type == "chunk":  # type: ignore
                chunks: Iterable[Union[str, BytesIO]] = [BytesIO(data_model.chunk)]  # type: ignore
            elif data_model.type == "chunks":  # type: ignore
                chunks = (BytesIO(c) for c in data_model.chunks)  # type: ignore
            elif data_model.type == "file":  # type: ignore
                chunks = [data_model.file]  # type: ignore
            elif data_model.type == "files":  # type: ignore
                chunks = data_model.files  # type: ignore
            elif data_model.type == "inline-json":  # type: ignore
                chunks = [BytesIO(data_model.as_json())]  # type: ignore
            else:
                raise Exception(
                    f"Invalid serialized data type: {type(data_model)}. Available types: {', '.join(SERIALIZE_TYPES)}"
                )

            chunk_ids = []
            for item in zip(serialized_value.get_cids_for_key(key), chunks):
                cid = item[0]
                _chunk = item[1]
                addr: HashAddress = self.hashfs.put_with_precomputed_hash(
                    _chunk, str(cid)
                )
                chunk_ids.append(addr.id)

            scids = SerializedChunkIDs(
                chunk_id_list=chunk_ids,
                archive_id=self.archive_id,
                size=data_model.get_size(),
            )
            scids._data_registry = self.kiara_context.data_registry
            chunk_id_map[key] = scids

        pers_value = PersistedData(
            archive_id=self.archive_id,
            chunk_id_map=chunk_id_map,
            data_type=serialized_value.data_type,
            data_type_config=serialized_value.data_type_config,
            serialization_profile=serialized_value.serialization_profile,
            metadata=serialized_value.metadata,
        )

        return pers_value

    def _persist_value_pedigree(self, value: Value):

        manifest_hash = value.pedigree.instance_cid
        jobs_hash = value.pedigree.job_hash

        base_path = self.get_path(entity_type=EntityType.MANIFEST)
        manifest_folder = base_path / str(manifest_hash)
        manifest_folder.mkdir(parents=True, exist_ok=True)

        manifest_info_file = manifest_folder / "manifest.json"
        if not manifest_info_file.exists():
            manifest_info_file.write_text(value.pedigree.manifest_data_as_json())

        job_folder = manifest_folder / str(jobs_hash)

        job_folder.mkdir(parents=True, exist_ok=True)

        inputs_details_file_name = job_folder / "inputs.json"
        if not inputs_details_file_name.exists():
            inputs_details_file_name.write_text(orjson_dumps(value.pedigree.inputs))

        outputs_file_name = (
            job_folder
            / f"output__{value.pedigree_output_name}__value_id__{value.value_id}.json"
        )

        if outputs_file_name.exists():
            # if value.pedigree_output_name == "__void__":
            #     return
            # else:
            raise Exception(f"Can't write value '{value.value_id}': already exists.")
        else:
            outputs_file_name.touch()

        value_data_dir = (
            self.get_path(entity_type=EntityType.VALUE_DATA)
            / value.value_schema.type
            / str(value.value_hash)
        )
        target_file = value_data_dir / f"value_id__{value.value_id}.json"

        target_file.symlink_to(outputs_file_name)

destinies special

Classes

DestinyArchive (ABC)

Source code in kiara/registries/destinies/__init__.py

class DestinyArchive(abc.ABC):
    @abc.abstractmethod
    def get_destiny_archive_id(self) -> uuid.UUID:
        pass

    @abc.abstractmethod
    def get_all_value_ids(self) -> Set[uuid.UUID]:
        """Retrun a list of all value ids that have destinies stored in this archive."""

    @abc.abstractmethod
    def get_destiny_aliases_for_value(self, value_id: uuid.UUID) -> Optional[Set[str]]:
        """Retrieve all the destinies for the specified value within this archive.

        In case this archive discovers its value destinies dynamically, this can return 'None'.
        """

    @abc.abstractmethod
    def get_destiny(self, value_id: uuid.UUID, destiny: str) -> Destiny:
        pass

Methods

get_all_value_ids(self)

Retrun a list of all value ids that have destinies stored in this archive.

Source code in kiara/registries/destinies/__init__.py

@abc.abstractmethod
def get_all_value_ids(self) -> Set[uuid.UUID]:
    """Retrun a list of all value ids that have destinies stored in this archive."""

get_destiny(self, value_id, destiny)

Source code in kiara/registries/destinies/__init__.py

@abc.abstractmethod
def get_destiny(self, value_id: uuid.UUID, destiny: str) -> Destiny:
    pass

get_destiny_aliases_for_value(self, value_id)

Retrieve all the destinies for the specified value within this archive.

In case this archive discovers its value destinies dynamically, this can return 'None'.

Source code in kiara/registries/destinies/__init__.py

@abc.abstractmethod
def get_destiny_aliases_for_value(self, value_id: uuid.UUID) -> Optional[Set[str]]:
    """Retrieve all the destinies for the specified value within this archive.

    In case this archive discovers its value destinies dynamically, this can return 'None'.
    """

get_destiny_archive_id(self)

Source code in kiara/registries/destinies/__init__.py

@abc.abstractmethod
def get_destiny_archive_id(self) -> uuid.UUID:
    pass

DestinyStore (DestinyArchive)

Source code in kiara/registries/destinies/__init__.py

class DestinyStore(DestinyArchive):
    @abc.abstractmethod
    def persist_destiny(self, destiny: Destiny):
        pass

persist_destiny(self, destiny)

Source code in kiara/registries/destinies/__init__.py

@abc.abstractmethod
def persist_destiny(self, destiny: Destiny):
    pass

Modules

filesystem_store

logger

Classes

FileSystemDestinyArchive (DestinyArchive)

Source code in kiara/registries/destinies/filesystem_store.py

class FileSystemDestinyArchive(DestinyArchive):
    @classmethod
    def create_from_kiara_context(cls, kiara: "Kiara"):

        TODO = kiara_app_dirs.user_data_dir
        base_path = Path(TODO) / "destiny_store"
        base_path.mkdir(parents=True, exist_ok=True)
        result = cls(base_path=base_path, store_id=kiara.id)
        ID_REGISTRY.update_metadata(
            result.get_destiny_archive_id(), kiara_id=kiara.id, obj=result
        )
        return result

    def __init__(self, base_path: Path, store_id: uuid.UUID):

        if not base_path.is_dir():
            raise Exception(
                f"Can't create file system archive instance, base path does not exist or is not a folder: {base_path.as_posix()}."
            )

        self._store_id: uuid.UUID = store_id
        self._base_path: Path = base_path
        self._destinies_path: Path = self._base_path / "destinies"
        self._value_id_path: Path = self._base_path / "value_ids"

    @property
    def destiny_store_path(self) -> Path:
        return self._base_path

    def get_destiny_archive_id(self) -> uuid.UUID:
        return self._store_id

    def _translate_destiny_id_to_path(self, destiny_id: uuid.UUID) -> Path:

        tokens = str(destiny_id).split("-")
        destiny_path = (
            self._destinies_path.joinpath(*tokens[0:-1]) / f"{tokens[-1]}.json"
        )
        return destiny_path

    def _translate_destinies_path_to_id(self, destinies_path: Path) -> uuid.UUID:

        relative = destinies_path.relative_to(self._destinies_path).as_posix()[:-5]

        destninies_id = "-".join(relative.split(os.path.sep))

        return uuid.UUID(destninies_id)

    def _translate_value_id(self, value_id: uuid.UUID, destiny_alias: str) -> Path:

        tokens = str(value_id).split("-")
        value_id_path = self._value_id_path.joinpath(*tokens)

        full_path = value_id_path / f"{destiny_alias}.json"
        return full_path

    def _translate_value_id_path(self, value_path: Path) -> uuid.UUID:

        relative = value_path.relative_to(self._value_id_path)

        value_id_str = "-".join(relative.as_posix().split(os.path.sep))
        return uuid.UUID(value_id_str)

    def _translate_alias_path(self, alias_path: Path) -> Tuple[uuid.UUID, str]:

        value_id = self._translate_value_id_path(alias_path.parent)

        alias = alias_path.name[0:-5]

        return value_id, alias

    def get_all_value_ids(self) -> Set[uuid.UUID]:

        all_root_folders = self._value_id_path.glob("*/*/*/*/*")

        result = set()
        for folder in all_root_folders:
            if not folder.is_dir():
                continue

            value_id = self._translate_value_id_path(folder)
            result.add(value_id)

        return result

    def get_destiny_aliases_for_value(self, value_id: uuid.UUID) -> Set[str]:

        tokens = str(value_id).split("-")
        value_id_path = self._value_id_path.joinpath(*tokens)

        aliases = value_id_path.glob("*.json")

        return set(a.name[0:-5] for a in aliases)

    def get_destiny(self, value_id: uuid.UUID, destiny_alias: str) -> Destiny:

        tokens = str(value_id).split("-")
        value_id_path = self._value_id_path.joinpath(*tokens)

        destiny_path = value_id_path / f"{destiny_alias}.json"

        destiny_data = orjson.loads(destiny_path.read_text())

        destiny = Destiny.construct(**destiny_data)
        return destiny

destiny_store_path: Path property readonly

Methods

create_from_kiara_context(kiara) classmethod

Source code in kiara/registries/destinies/filesystem_store.py

@classmethod
def create_from_kiara_context(cls, kiara: "Kiara"):

    TODO = kiara_app_dirs.user_data_dir
    base_path = Path(TODO) / "destiny_store"
    base_path.mkdir(parents=True, exist_ok=True)
    result = cls(base_path=base_path, store_id=kiara.id)
    ID_REGISTRY.update_metadata(
        result.get_destiny_archive_id(), kiara_id=kiara.id, obj=result
    )
    return result

get_all_value_ids(self)

Retrun a list of all value ids that have destinies stored in this archive.

Source code in kiara/registries/destinies/filesystem_store.py

def get_all_value_ids(self) -> Set[uuid.UUID]:

    all_root_folders = self._value_id_path.glob("*/*/*/*/*")

    result = set()
    for folder in all_root_folders:
        if not folder.is_dir():
            continue

        value_id = self._translate_value_id_path(folder)
        result.add(value_id)

    return result

get_destiny(self, value_id, destiny_alias)

Source code in kiara/registries/destinies/filesystem_store.py

def get_destiny(self, value_id: uuid.UUID, destiny_alias: str) -> Destiny:

    tokens = str(value_id).split("-")
    value_id_path = self._value_id_path.joinpath(*tokens)

    destiny_path = value_id_path / f"{destiny_alias}.json"

    destiny_data = orjson.loads(destiny_path.read_text())

    destiny = Destiny.construct(**destiny_data)
    return destiny

get_destiny_aliases_for_value(self, value_id)

Retrieve all the destinies for the specified value within this archive.

In case this archive discovers its value destinies dynamically, this can return 'None'.

Source code in kiara/registries/destinies/filesystem_store.py

def get_destiny_aliases_for_value(self, value_id: uuid.UUID) -> Set[str]:

    tokens = str(value_id).split("-")
    value_id_path = self._value_id_path.joinpath(*tokens)

    aliases = value_id_path.glob("*.json")

    return set(a.name[0:-5] for a in aliases)

get_destiny_archive_id(self)

Source code in kiara/registries/destinies/filesystem_store.py

def get_destiny_archive_id(self) -> uuid.UUID:
    return self._store_id

FileSystemDestinyStore (FileSystemDestinyArchive, DestinyStore)

Source code in kiara/registries/destinies/filesystem_store.py

class FileSystemDestinyStore(FileSystemDestinyArchive, DestinyStore):
    def persist_destiny(self, destiny: Destiny):

        destiny_path = self._translate_destiny_id_to_path(destiny_id=destiny.destiny_id)
        destiny_path.parent.mkdir(parents=True, exist_ok=True)
        destiny_path.write_text(destiny.json())

        for value_id in destiny.fixed_inputs.values():

            path = self._translate_value_id(
                value_id=value_id, destiny_alias=destiny.destiny_alias
            )
            if path.exists():
                logger.debug("replace.destiny.file", path=path.as_posix())
                path.unlink()
                # raise Exception(
                #     f"Can't persist destiny '{destiny.destiny_id}': already persisted."
                # )

            path.parent.mkdir(parents=True, exist_ok=True)
            path.symlink_to(destiny_path)

persist_destiny(self, destiny)

Source code in kiara/registries/destinies/filesystem_store.py

def persist_destiny(self, destiny: Destiny):

    destiny_path = self._translate_destiny_id_to_path(destiny_id=destiny.destiny_id)
    destiny_path.parent.mkdir(parents=True, exist_ok=True)
    destiny_path.write_text(destiny.json())

    for value_id in destiny.fixed_inputs.values():

        path = self._translate_value_id(
            value_id=value_id, destiny_alias=destiny.destiny_alias
        )
        if path.exists():
            logger.debug("replace.destiny.file", path=path.as_posix())
            path.unlink()
            # raise Exception(
            #     f"Can't persist destiny '{destiny.destiny_id}': already persisted."
            # )

        path.parent.mkdir(parents=True, exist_ok=True)
        path.symlink_to(destiny_path)

registry

Classes

DestinyRegistry

Source code in kiara/registries/destinies/registry.py

class DestinyRegistry(object):
    def __init__(self, kiara: "Kiara"):

        self._kiara: Kiara = kiara
        self._destiny_archives: Dict[str, DestinyArchive] = {}
        self._default_destiny_store: Optional[str] = None
        default_metadata_archive = FileSystemDestinyStore.create_from_kiara_context(
            self._kiara
        )
        self.register_destiny_archive("metadata", default_metadata_archive)

        self._all_values: Optional[Dict[uuid.UUID, Set[str]]] = None
        self._cached_value_aliases: Dict[uuid.UUID, Dict[str, Optional[Destiny]]] = {}

        self._destinies: Dict[uuid.UUID, Destiny] = {}
        self._destinies_by_value: Dict[uuid.UUID, Dict[str, Destiny]] = {}
        self._destiny_store_map: Dict[uuid.UUID, str] = {}

    @property
    def default_destiny_store(self) -> DestinyStore:

        if self._default_destiny_store is None:
            raise Exception("No default destiny store set (yet).")

        return self._destiny_archives[self._default_destiny_store]  # type: ignore

    def register_destiny_archive(self, registered_name: str, alias_store: DestinyStore):

        if not registered_name.isalnum():
            raise Exception(
                f"Can't register destiny archive with name '{registered_name}: name must only contain alphanumeric characters.'"
            )

        if registered_name in self._destiny_archives.keys():
            raise Exception(
                f"Can't register alias store, store id already registered: {registered_name}."
            )

        self._destiny_archives[registered_name] = alias_store

        if self._default_destiny_store is None and isinstance(
            alias_store, DestinyStore
        ):
            self._default_destiny_store = registered_name

    def _split_alias(self, alias: str) -> Tuple[str, str]:

        if "." not in alias:
            assert self._default_destiny_store is not None
            return self._default_destiny_store, alias

        store_id, rest = alias.split(".", maxsplit=1)

        if store_id not in self._destiny_archives.keys():
            raise Exception(
                f"Invalid alias '{alias}', no store with prefix '{store_id}' registered. Available prefixes: {', '.join(self._destiny_archives.keys())}"
            )

        return (store_id, rest)

    def add_destiny(
        self,
        destiny_alias: str,
        values: Dict[str, uuid.UUID],
        manifest: Manifest,
        result_field_name: Optional[str] = None,
    ) -> Destiny:
        """Add a destiny for one (or in some rare cases several) values.

        A destiny alias must be unique for every one of the involved input values.
        """

        if not values:
            raise Exception("Can't add destiny, no values provided.")

        store_id, alias = self._split_alias(destiny_alias)

        destiny = Destiny.create_from_values(
            kiara=self._kiara,
            destiny_alias=alias,
            manifest=manifest,
            result_field_name=result_field_name,
            values=values,
        )

        for value_id in destiny.fixed_inputs.values():
            self._destinies[destiny.destiny_id] = destiny
            # TODO: store history?
            self._destinies_by_value.setdefault(value_id, {})[destiny_alias] = destiny
            self._cached_value_aliases.setdefault(value_id, {})[destiny_alias] = destiny

        self._destiny_store_map[destiny.destiny_id] = store_id

        return destiny

    def get_destiny(self, value_id: uuid.UUID, destiny_alias: str) -> Destiny:

        destiny = self._destinies_by_value.get(value_id, {}).get(destiny_alias, None)
        if destiny is None:
            raise Exception(
                f"No destiny '{destiny_alias}' available for value '{value_id}'."
            )

        return destiny

    @property
    def _all_values_store_map(self) -> Dict[uuid.UUID, Set[str]]:

        if self._all_values is not None:
            return self._all_values

        all_values: Dict[uuid.UUID, Set[str]] = {}
        for archive_id, archive in self._destiny_archives.items():

            all_value_ids = archive.get_all_value_ids()
            for v_id in all_value_ids:
                all_values.setdefault(v_id, set()).add(archive_id)

        self._all_values = all_values
        return self._all_values

    @property
    def all_values(self) -> Iterable[uuid.UUID]:

        all_stored_values = set(self._all_values_store_map.keys())
        all_stored_values.update(self._destinies_by_value.keys())
        return all_stored_values

    def get_destiny_aliases_for_value(
        self, value_id: uuid.UUID, alias_filter: Optional[str] = None
    ) -> Iterable[str]:

        # TODO: cache the result of this

        if alias_filter is not None:
            raise NotImplementedError()

        all_stores = self._all_values_store_map.get(value_id)
        aliases: Set[str] = set()
        if all_stores:
            for prefix in all_stores:
                all_aliases = self._destiny_archives[
                    prefix
                ].get_destiny_aliases_for_value(value_id=value_id)
                if all_aliases is not None:
                    aliases.update((f"{prefix}.{a}" for a in all_aliases))

        current = self._destinies_by_value.get(value_id, None)
        if current:
            aliases.update(current.keys())

        return sorted(aliases)

    # def get_destinies_for_value(
    #     self,
    #     value_id: uuid.UUID,
    #     destiny_alias_filter: Optional[str] = None
    # ) -> Mapping[str, Destiny]:
    #
    #
    #
    #     return self._destinies_by_value.get(value_id, {})

    def resolve_destiny(self, destiny: Destiny) -> Value:

        results = self._kiara.job_registry.execute_and_retrieve(
            manifest=destiny, inputs=destiny.merged_inputs
        )
        value = results.get_value_obj(field_name=destiny.result_field_name)

        destiny.result_value_id = value.value_id

        return value

    def attach_as_property(
        self,
        destiny: Union[uuid.UUID, Destiny],
        field_names: Optional[Iterable[str]] = None,
    ):

        if field_names:
            raise NotImplementedError()

        if isinstance(destiny, uuid.UUID):
            destiny = self._destinies[destiny]

        values = self._kiara.data_registry.load_values(destiny.fixed_inputs)

        already_stored: List[uuid.UUID] = []
        for v in values.values():
            if v.is_stored:
                already_stored.append(v.value_id)

        if already_stored:
            stored = (str(v) for v in already_stored)
            raise Exception(
                f"Can't attach destiny as property, value(s) already stored: {', '.join(stored)}"
            )

        store_id = self._destiny_store_map[destiny.destiny_id]

        full_path = f"{store_id}.{destiny.destiny_alias}"

        for v in values.values():
            assert destiny.result_value_id is not None
            v.add_property(
                value_id=destiny.result_value_id,
                property_path=full_path,
                add_origin_to_property_value=True,
            )

    def store_destiny(self, destiny_id: Union[Destiny, uuid.UUID]):

        try:
            _destiny_id: uuid.UUID = destiny_id.destiny_id  # type: ignore
        except Exception:
            # just in case this is a 'Destiny' object
            _destiny_id = destiny_id  # type: ignore

        store_id = self._destiny_store_map[_destiny_id]
        destiny = self._destinies[_destiny_id]
        store: DestinyStore = self._destiny_archives[store_id]  # type: ignore

        if not isinstance(store, DestinyStore):
            full_alias = f"{store_id}.{destiny.destiny_alias}"
            raise Exception(
                f"Can't store destiny '{full_alias}': prefix '{store_id}' not writable in this kiara context."
            )

        store.persist_destiny(destiny=destiny)

all_values: Iterable[uuid.UUID] property readonly

default_destiny_store: DestinyStore property readonly

Methods

add_destiny(self, destiny_alias, values, manifest, result_field_name=None)

Add a destiny for one (or in some rare cases several) values.

A destiny alias must be unique for every one of the involved input values.

Source code in kiara/registries/destinies/registry.py

def add_destiny(
    self,
    destiny_alias: str,
    values: Dict[str, uuid.UUID],
    manifest: Manifest,
    result_field_name: Optional[str] = None,
) -> Destiny:
    """Add a destiny for one (or in some rare cases several) values.

    A destiny alias must be unique for every one of the involved input values.
    """

    if not values:
        raise Exception("Can't add destiny, no values provided.")

    store_id, alias = self._split_alias(destiny_alias)

    destiny = Destiny.create_from_values(
        kiara=self._kiara,
        destiny_alias=alias,
        manifest=manifest,
        result_field_name=result_field_name,
        values=values,
    )

    for value_id in destiny.fixed_inputs.values():
        self._destinies[destiny.destiny_id] = destiny
        # TODO: store history?
        self._destinies_by_value.setdefault(value_id, {})[destiny_alias] = destiny
        self._cached_value_aliases.setdefault(value_id, {})[destiny_alias] = destiny

    self._destiny_store_map[destiny.destiny_id] = store_id

    return destiny

attach_as_property(self, destiny, field_names=None)

Source code in kiara/registries/destinies/registry.py

def attach_as_property(
    self,
    destiny: Union[uuid.UUID, Destiny],
    field_names: Optional[Iterable[str]] = None,
):

    if field_names:
        raise NotImplementedError()

    if isinstance(destiny, uuid.UUID):
        destiny = self._destinies[destiny]

    values = self._kiara.data_registry.load_values(destiny.fixed_inputs)

    already_stored: List[uuid.UUID] = []
    for v in values.values():
        if v.is_stored:
            already_stored.append(v.value_id)

    if already_stored:
        stored = (str(v) for v in already_stored)
        raise Exception(
            f"Can't attach destiny as property, value(s) already stored: {', '.join(stored)}"
        )

    store_id = self._destiny_store_map[destiny.destiny_id]

    full_path = f"{store_id}.{destiny.destiny_alias}"

    for v in values.values():
        assert destiny.result_value_id is not None
        v.add_property(
            value_id=destiny.result_value_id,
            property_path=full_path,
            add_origin_to_property_value=True,
        )

get_destiny(self, value_id, destiny_alias)

Source code in kiara/registries/destinies/registry.py

def get_destiny(self, value_id: uuid.UUID, destiny_alias: str) -> Destiny:

    destiny = self._destinies_by_value.get(value_id, {}).get(destiny_alias, None)
    if destiny is None:
        raise Exception(
            f"No destiny '{destiny_alias}' available for value '{value_id}'."
        )

    return destiny

get_destiny_aliases_for_value(self, value_id, alias_filter=None)

Source code in kiara/registries/destinies/registry.py

def get_destiny_aliases_for_value(
    self, value_id: uuid.UUID, alias_filter: Optional[str] = None
) -> Iterable[str]:

    # TODO: cache the result of this

    if alias_filter is not None:
        raise NotImplementedError()

    all_stores = self._all_values_store_map.get(value_id)
    aliases: Set[str] = set()
    if all_stores:
        for prefix in all_stores:
            all_aliases = self._destiny_archives[
                prefix
            ].get_destiny_aliases_for_value(value_id=value_id)
            if all_aliases is not None:
                aliases.update((f"{prefix}.{a}" for a in all_aliases))

    current = self._destinies_by_value.get(value_id, None)
    if current:
        aliases.update(current.keys())

    return sorted(aliases)

register_destiny_archive(self, registered_name, alias_store)

Source code in kiara/registries/destinies/registry.py

def register_destiny_archive(self, registered_name: str, alias_store: DestinyStore):

    if not registered_name.isalnum():
        raise Exception(
            f"Can't register destiny archive with name '{registered_name}: name must only contain alphanumeric characters.'"
        )

    if registered_name in self._destiny_archives.keys():
        raise Exception(
            f"Can't register alias store, store id already registered: {registered_name}."
        )

    self._destiny_archives[registered_name] = alias_store

    if self._default_destiny_store is None and isinstance(
        alias_store, DestinyStore
    ):
        self._default_destiny_store = registered_name

resolve_destiny(self, destiny)

Source code in kiara/registries/destinies/registry.py

def resolve_destiny(self, destiny: Destiny) -> Value:

    results = self._kiara.job_registry.execute_and_retrieve(
        manifest=destiny, inputs=destiny.merged_inputs
    )
    value = results.get_value_obj(field_name=destiny.result_field_name)

    destiny.result_value_id = value.value_id

    return value

store_destiny(self, destiny_id)

Source code in kiara/registries/destinies/registry.py

def store_destiny(self, destiny_id: Union[Destiny, uuid.UUID]):

    try:
        _destiny_id: uuid.UUID = destiny_id.destiny_id  # type: ignore
    except Exception:
        # just in case this is a 'Destiny' object
        _destiny_id = destiny_id  # type: ignore

    store_id = self._destiny_store_map[_destiny_id]
    destiny = self._destinies[_destiny_id]
    store: DestinyStore = self._destiny_archives[store_id]  # type: ignore

    if not isinstance(store, DestinyStore):
        full_alias = f"{store_id}.{destiny.destiny_alias}"
        raise Exception(
            f"Can't store destiny '{full_alias}': prefix '{store_id}' not writable in this kiara context."
        )

    store.persist_destiny(destiny=destiny)

environment special

Classes

EnvironmentRegistry

Source code in kiara/registries/environment/__init__.py

class EnvironmentRegistry(object):

    _instance = None

    @classmethod
    def instance(cls):
        """The default *kiara* context. In most cases, it's recommended you create and manage your own, though."""

        if cls._instance is None:
            cls._instance = EnvironmentRegistry()
        return cls._instance

    def __init__(
        self,
    ):
        self._environments: Optional[Dict[str, RuntimeEnvironment]] = None

        self._full_env_model: Optional[BaseModel] = None

    def get_environment_for_cid(self, env_cid: str) -> RuntimeEnvironment:

        envs = [env for env in self.environments.values() if env.instance_id == env_cid]
        if len(envs) == 0:
            raise Exception(f"No environment with id '{env_cid}' available.")
        elif len(envs) > 1:
            raise Exception(
                f"Multipe environments with id '{env_cid}' available. This is most likely a bug."
            )
        return envs[0]

    @property
    def environments(self) -> Mapping[str, RuntimeEnvironment]:
        """Return all environments in this kiara runtime context."""

        if self._environments is not None:
            return self._environments

        import kiara.models.runtime_environment.kiara  # noqa
        import kiara.models.runtime_environment.operating_system  # noqa
        import kiara.models.runtime_environment.python  # noqa

        subclasses: Iterable[Type[RuntimeEnvironment]] = _get_all_subclasses(
            RuntimeEnvironment  # type: ignore
        )
        envs = {}
        for sc in subclasses:
            if inspect.isabstract(sc):
                if is_debug():
                    logger.warning("class_loading.ignore_subclass", subclass=sc)
                else:
                    logger.debug("class_loading.ignore_subclass", subclass=sc)

            name = sc.get_environment_type_name()
            envs[name] = sc.create_environment_model()

        self._environments = envs
        return self._environments

    @property
    def full_model(self) -> BaseModel:
        """A model containing all environment data, incl. schemas and hashes of each sub-environment."""

        if self._full_env_model is not None:
            return self._full_env_model

        attrs = {k: (v.__class__, ...) for k, v in self.environments.items()}

        models = {}
        hashes = {}
        schemas = {}

        for k, v in attrs.items():
            name = to_camel_case(f"{k}_environment")
            k_cls: Type[RuntimeEnvironment] = create_model(
                name,
                __base__=v[0],
                metadata_hash=(
                    str,
                    Field(
                        description="The hash for this metadata (excl. this and the 'metadata_schema' field)."
                    ),
                ),
                metadata_schema=(
                    str,
                    Field(
                        description="JsonSchema describing this metadata (excl. this and the 'metadata_hash' field)."
                    ),
                ),
            )
            models[k] = (
                k_cls,
                Field(description=f"Metadata describing the {k} environment."),
            )
            schemas[k] = v[0].schema_json()
            hashes[k] = self.environments[k].instance_cid

        cls: Type[BaseModel] = create_model("KiaraRuntimeInfo", **models)  # type: ignore
        data = {}
        for k2, v2 in self.environments.items():
            d = v2.dict()
            assert "metadata_hash" not in d.keys()
            assert "metadata_schema" not in d.keys()
            d["metadata_hash"] = str(hashes[k2])
            d["metadata_schema"] = schemas[k]
            data[k2] = d
        model = cls.construct(**data)  # type: ignore
        self._full_env_model = model
        return self._full_env_model

    def create_renderable(self, **config: Any):

        full_details = config.get("full_details", False)

        table = Table(show_header=True, box=box.SIMPLE)
        table.add_column("environment key", style="b")
        table.add_column("details")

        for env_name, env in self.environments.items():
            renderable = env.create_renderable(summary=not full_details)
            table.add_row(env_name, renderable)

        return table

Attributes

environments: Mapping[str, kiara.models.runtime_environment.RuntimeEnvironment] property readonly

Return all environments in this kiara runtime context.

full_model: BaseModel property readonly

A model containing all environment data, incl. schemas and hashes of each sub-environment.

Methods

create_renderable(self, **config)

Source code in kiara/registries/environment/__init__.py

def create_renderable(self, **config: Any):

    full_details = config.get("full_details", False)

    table = Table(show_header=True, box=box.SIMPLE)
    table.add_column("environment key", style="b")
    table.add_column("details")

    for env_name, env in self.environments.items():
        renderable = env.create_renderable(summary=not full_details)
        table.add_row(env_name, renderable)

    return table

get_environment_for_cid(self, env_cid)

Source code in kiara/registries/environment/__init__.py

def get_environment_for_cid(self, env_cid: str) -> RuntimeEnvironment:

    envs = [env for env in self.environments.values() if env.instance_id == env_cid]
    if len(envs) == 0:
        raise Exception(f"No environment with id '{env_cid}' available.")
    elif len(envs) > 1:
        raise Exception(
            f"Multipe environments with id '{env_cid}' available. This is most likely a bug."
        )
    return envs[0]

instance() classmethod

The default kiara context. In most cases, it's recommended you create and manage your own, though.

Source code in kiara/registries/environment/__init__.py

@classmethod
def instance(cls):
    """The default *kiara* context. In most cases, it's recommended you create and manage your own, though."""

    if cls._instance is None:
        cls._instance = EnvironmentRegistry()
    return cls._instance

events special

AsyncEventListener (Protocol)

Source code in kiara/registries/events/__init__.py

class AsyncEventListener(Protocol):
    def wait_for_processing(self, processing_id: Any):
        pass

wait_for_processing(self, processing_id)

Source code in kiara/registries/events/__init__.py

def wait_for_processing(self, processing_id: Any):
    pass

EventListener (Protocol)

Source code in kiara/registries/events/__init__.py

class EventListener(Protocol):
    def handle_events(self, *events: KiaraEvent) -> Any:
        pass

handle_events(self, *events)

Source code in kiara/registries/events/__init__.py

def handle_events(self, *events: KiaraEvent) -> Any:
    pass

EventProducer (Protocol)

Source code in kiara/registries/events/__init__.py

class EventProducer(Protocol):

    pass

    # def suppoerted_event_types(self) -> Iterable[Type[KiaraEvent]]:
    #     pass

metadata

CreateMetadataDestinies

Source code in kiara/registries/events/metadata.py

class CreateMetadataDestinies(object):
    def __init__(self, kiara: "Kiara"):

        self._kiara: Kiara = kiara
        self._skip_internal_types: bool = True

    def supported_event_types(self) -> Iterable[str]:
        return ["value_created", "value_registered"]

    def handle_events(self, *events: KiaraEvent) -> Any:

        for event in events:
            if event.get_event_type() == "value_created":  # type: ignore
                self.attach_metadata(event.value)  # type: ignore

        for event in events:
            if event.get_event_type() == "value_registered":  # type: ignore
                self.resolve_all_metadata(event.value)  # type: ignore

    def attach_metadata(self, value: Value):

        assert not value.is_stored

        if self._skip_internal_types:

            if value.value_schema.type == "any":
                return
            lineage = self._kiara.type_registry.get_type_lineage(
                value.value_schema.type
            )
            if "any" not in lineage:
                return

        op_type: ExtractMetadataOperationType = self._kiara.operation_registry.get_operation_type("extract_metadata")  # type: ignore
        operations = op_type.get_operations_for_data_type(value.value_schema.type)
        for metadata_key, op in operations.items():
            op_details: ExtractMetadataDetails = op.operation_details  # type: ignore
            input_field_name = op_details.input_field_name
            result_field_name = op_details.result_field_name
            self._kiara.destiny_registry.add_destiny(
                destiny_alias=f"metadata.{metadata_key}",
                values={input_field_name: value.value_id},
                manifest=op,
                result_field_name=result_field_name,
            )

    def resolve_all_metadata(self, value: Value):

        if self._skip_internal_types:

            lineage = self._kiara.type_registry.get_type_lineage(
                value.value_schema.type
            )
            if "any" not in lineage:
                return

        assert not value.is_stored

        aliases = self._kiara.destiny_registry.get_destiny_aliases_for_value(
            value_id=value.value_id
        )

        for alias in aliases:
            destiny = self._kiara.destiny_registry.get_destiny(
                value_id=value.value_id, destiny_alias=alias
            )
            self._kiara.destiny_registry.resolve_destiny(destiny)
            self._kiara.destiny_registry.attach_as_property(destiny)

attach_metadata(self, value)

Source code in kiara/registries/events/metadata.py

def attach_metadata(self, value: Value):

    assert not value.is_stored

    if self._skip_internal_types:

        if value.value_schema.type == "any":
            return
        lineage = self._kiara.type_registry.get_type_lineage(
            value.value_schema.type
        )
        if "any" not in lineage:
            return

    op_type: ExtractMetadataOperationType = self._kiara.operation_registry.get_operation_type("extract_metadata")  # type: ignore
    operations = op_type.get_operations_for_data_type(value.value_schema.type)
    for metadata_key, op in operations.items():
        op_details: ExtractMetadataDetails = op.operation_details  # type: ignore
        input_field_name = op_details.input_field_name
        result_field_name = op_details.result_field_name
        self._kiara.destiny_registry.add_destiny(
            destiny_alias=f"metadata.{metadata_key}",
            values={input_field_name: value.value_id},
            manifest=op,
            result_field_name=result_field_name,
        )

handle_events(self, *events)

Source code in kiara/registries/events/metadata.py

def handle_events(self, *events: KiaraEvent) -> Any:

    for event in events:
        if event.get_event_type() == "value_created":  # type: ignore
            self.attach_metadata(event.value)  # type: ignore

    for event in events:
        if event.get_event_type() == "value_registered":  # type: ignore
            self.resolve_all_metadata(event.value)  # type: ignore

resolve_all_metadata(self, value)

Source code in kiara/registries/events/metadata.py

def resolve_all_metadata(self, value: Value):

    if self._skip_internal_types:

        lineage = self._kiara.type_registry.get_type_lineage(
            value.value_schema.type
        )
        if "any" not in lineage:
            return

    assert not value.is_stored

    aliases = self._kiara.destiny_registry.get_destiny_aliases_for_value(
        value_id=value.value_id
    )

    for alias in aliases:
        destiny = self._kiara.destiny_registry.get_destiny(
            value_id=value.value_id, destiny_alias=alias
        )
        self._kiara.destiny_registry.resolve_destiny(destiny)
        self._kiara.destiny_registry.attach_as_property(destiny)

supported_event_types(self)

Source code in kiara/registries/events/metadata.py

def supported_event_types(self) -> Iterable[str]:
    return ["value_created", "value_registered"]

registry

AllEvents (KiaraEvent) pydantic-model

Source code in kiara/registries/events/registry.py

class AllEvents(KiaraEvent):
    pass

EventRegistry

Source code in kiara/registries/events/registry.py

class EventRegistry(object):
    def __init__(self, kiara: "Kiara"):

        self._kiara: Kiara = kiara
        self._producers: Dict[uuid.UUID, EventProducer] = {}
        self._listeners: Dict[uuid.UUID, EventListener] = {}
        self._subscriptions: Dict[uuid.UUID, List[str]] = {}

    def add_producer(self, producer: EventProducer) -> Callable:

        producer_id = ID_REGISTRY.generate(
            obj=producer, comment="adding event producer"
        )
        func = partial(self.handle_events, producer_id)
        return func

    def add_listener(self, listener, *subscriptions: str):

        if not subscriptions:
            _subscriptions = ["*"]
        else:
            _subscriptions = list(subscriptions)

        listener_id = ID_REGISTRY.generate(
            obj=listener, comment="adding event listener"
        )
        self._listeners[listener_id] = listener
        self._subscriptions[listener_id] = _subscriptions

    def _matches_subscription(
        self, events: Iterable[KiaraEvent], subscriptions: Iterable[str]
    ) -> Iterable[KiaraEvent]:

        result = []
        for subscription in subscriptions:
            for event in events:
                match = fnmatch.filter([event.get_event_type()], subscription)
                if match:
                    result.append(event)

        return result

    def handle_events(self, producer_id: uuid.UUID, *events: KiaraEvent):

        event_targets: Dict[uuid.UUID, List[KiaraEvent]] = {}

        for l_id, listener in self._listeners.items():
            matches = self._matches_subscription(
                events=events, subscriptions=self._subscriptions[l_id]
            )
            if matches:
                event_targets.setdefault(l_id, []).extend(matches)

        responses = {}
        for l_id, l_events in event_targets.items():
            listener = self._listeners[l_id]
            response = listener.handle_events(*l_events)
            responses[l_id] = response

        for l_id, response in responses.items():
            if response is None:
                continue

            a_listener: AsyncEventListener = self._listeners[l_id]  # type: ignore
            if not hasattr(a_listener, "wait_for_processing"):
                raise Exception(
                    "Can't wait for processing of event for listener: listener does not provide 'wait_for_processing' method."
                )
            a_listener.wait_for_processing(response)

add_listener(self, listener, *subscriptions)

Source code in kiara/registries/events/registry.py

def add_listener(self, listener, *subscriptions: str):

    if not subscriptions:
        _subscriptions = ["*"]
    else:
        _subscriptions = list(subscriptions)

    listener_id = ID_REGISTRY.generate(
        obj=listener, comment="adding event listener"
    )
    self._listeners[listener_id] = listener
    self._subscriptions[listener_id] = _subscriptions

add_producer(self, producer)

Source code in kiara/registries/events/registry.py

def add_producer(self, producer: EventProducer) -> Callable:

    producer_id = ID_REGISTRY.generate(
        obj=producer, comment="adding event producer"
    )
    func = partial(self.handle_events, producer_id)
    return func

handle_events(self, producer_id, *events)

Source code in kiara/registries/events/registry.py

def handle_events(self, producer_id: uuid.UUID, *events: KiaraEvent):

    event_targets: Dict[uuid.UUID, List[KiaraEvent]] = {}

    for l_id, listener in self._listeners.items():
        matches = self._matches_subscription(
            events=events, subscriptions=self._subscriptions[l_id]
        )
        if matches:
            event_targets.setdefault(l_id, []).extend(matches)

    responses = {}
    for l_id, l_events in event_targets.items():
        listener = self._listeners[l_id]
        response = listener.handle_events(*l_events)
        responses[l_id] = response

    for l_id, response in responses.items():
        if response is None:
            continue

        a_listener: AsyncEventListener = self._listeners[l_id]  # type: ignore
        if not hasattr(a_listener, "wait_for_processing"):
            raise Exception(
                "Can't wait for processing of event for listener: listener does not provide 'wait_for_processing' method."
            )
        a_listener.wait_for_processing(response)

ids special

ID_REGISTRY

logger

IdRegistry

Source code in kiara/registries/ids/__init__.py

class IdRegistry(object):
    def __init__(self):
        self._ids: Dict[uuid.UUID, Dict[Type, Dict[str, Any]]] = {}
        self._objs: Dict[uuid.UUID, WeakValueDictionary[Type, Any]] = {}

    def generate(
        self,
        id: Optional[uuid.UUID] = None,
        obj_type: Optional[Type] = None,
        obj: Optional[Any] = None,
        **metadata: Any
    ):

        if id is None:
            id = uuid.uuid4()

        if is_debug() or is_develop():

            # logger.debug("generate.id", id=id, metadata=metadata)
            if obj_type is None:
                if obj:
                    obj_type = obj.__class__
                else:
                    obj_type = NO_TYPE_MARKER
            self._ids.setdefault(id, {}).setdefault(obj_type, {}).update(metadata)
            if obj:
                self._objs.setdefault(id, WeakValueDictionary())[obj_type] = obj

        return id

    def update_metadata(
        self,
        id: uuid.UUID,
        obj_type: Optional[Type] = None,
        obj: Optional[Any] = None,
        **metadata
    ):

        if not is_debug() and not is_develop():
            return

        if obj_type is None:
            if obj:
                obj_type = obj.__class__
            else:
                obj_type = NO_TYPE_MARKER
        self._ids.setdefault(id, {}).setdefault(obj_type, {}).update(metadata)
        if obj:
            self._objs.setdefault(id, WeakValueDictionary())[obj_type] = obj

generate(self, id=None, obj_type=None, obj=None, **metadata)

Source code in kiara/registries/ids/__init__.py

def generate(
    self,
    id: Optional[uuid.UUID] = None,
    obj_type: Optional[Type] = None,
    obj: Optional[Any] = None,
    **metadata: Any
):

    if id is None:
        id = uuid.uuid4()

    if is_debug() or is_develop():

        # logger.debug("generate.id", id=id, metadata=metadata)
        if obj_type is None:
            if obj:
                obj_type = obj.__class__
            else:
                obj_type = NO_TYPE_MARKER
        self._ids.setdefault(id, {}).setdefault(obj_type, {}).update(metadata)
        if obj:
            self._objs.setdefault(id, WeakValueDictionary())[obj_type] = obj

    return id

update_metadata(self, id, obj_type=None, obj=None, **metadata)

Source code in kiara/registries/ids/__init__.py

def update_metadata(
    self,
    id: uuid.UUID,
    obj_type: Optional[Type] = None,
    obj: Optional[Any] = None,
    **metadata
):

    if not is_debug() and not is_develop():
        return

    if obj_type is None:
        if obj:
            obj_type = obj.__class__
        else:
            obj_type = NO_TYPE_MARKER
    self._ids.setdefault(id, {}).setdefault(obj_type, {}).update(metadata)
    if obj:
        self._objs.setdefault(id, WeakValueDictionary())[obj_type] = obj

NO_TYPE_MARKER

Source code in kiara/registries/ids/__init__.py

class NO_TYPE_MARKER(object):
    pass

jobs special

MANIFEST_SUB_PATH

logger

Classes

JobArchive (BaseArchive)

Source code in kiara/registries/jobs/__init__.py

class JobArchive(BaseArchive):
    @abc.abstractmethod
    def find_matching_job_record(
        self, inputs_manifest: InputsManifest
    ) -> Optional[JobRecord]:
        pass

find_matching_job_record(self, inputs_manifest)

Source code in kiara/registries/jobs/__init__.py

@abc.abstractmethod
def find_matching_job_record(
    self, inputs_manifest: InputsManifest
) -> Optional[JobRecord]:
    pass

JobRegistry

Source code in kiara/registries/jobs/__init__.py

class JobRegistry(object):
    def __init__(self, kiara: "Kiara"):

        self._kiara: Kiara = kiara
        self._active_jobs: bidict[str, uuid.UUID] = bidict()
        self._failed_jobs: Dict[str, uuid.UUID] = {}
        self._finished_jobs: Dict[str, uuid.UUID] = {}
        self._archived_records: Dict[uuid.UUID, JobRecord] = {}

        self._processor: ModuleProcessor = SynchronousProcessor(kiara=self._kiara)
        self._processor.register_job_status_listener(self)
        self._job_archives: Dict[str, JobArchive] = {}
        self._default_job_store: Optional[str] = None

        self._event_callback = self._kiara.event_registry.add_producer(self)

        # default_archive = FileSystemJobStore.create_from_kiara_context(self._kiara)
        # self.register_job_archive(default_archive, store_alias=DEFAULT_STORE_MARKER)

        # default_file_store = self._kiara.data_registry.get_archive(DEFAULT_STORE_MARKER)
        # self.register_job_archive(default_file_store, store_alias="default_data_store")  # type: ignore

    def suppoerted_event_types(self) -> Iterable[Type[KiaraEvent]]:

        return [JobArchiveAddedEvent, JobRecordPreStoreEvent, JobRecordStoredEvent]

    def register_job_archive(self, archive: JobArchive, alias: Optional[str] = None):

        if alias is None:
            alias = str(archive.archive_id)

        if alias in self._job_archives.keys():
            raise Exception(
                f"Can't register job store, store id already registered: {alias}."
            )

        self._job_archives[alias] = archive

        is_store = False
        is_default_store = False
        if isinstance(archive, JobStore):
            is_store = True
            if self._default_job_store is None:
                self._default_job_store = alias

        event = JobArchiveAddedEvent.construct(
            kiara_id=self._kiara.id,
            job_archive_id=archive.archive_id,
            job_archive_alias=alias,
            is_store=is_store,
            is_default_store=is_default_store,
        )
        self._event_callback(event)

    @property
    def default_job_store(self) -> str:

        if self._default_job_store is None:
            raise Exception("No default job store set (yet).")
        return self._default_job_store  # type: ignore

    def get_archive(self, store_id: Optional[str] = None) -> JobArchive:

        if store_id is None:
            store_id = self.default_job_store
            if store_id is None:
                raise Exception("Can't retrieve deafult job archive, none set (yet).")

        return self._job_archives[store_id]

    def job_status_changed(
        self, job_id: uuid.UUID, old_status: Optional[JobStatus], new_status: JobStatus
    ):

        # print(f"JOB STATUS CHANGED: {job_id} - {old_status} - {new_status.value}")
        if job_id in self._active_jobs.values() and new_status is JobStatus.FAILED:
            job_hash = self._active_jobs.inverse.pop(job_id)
            self._failed_jobs[job_hash] = job_id
        elif job_id in self._active_jobs.values() and new_status is JobStatus.SUCCESS:
            job_hash = self._active_jobs.inverse.pop(job_id)

            job_record = self._processor.get_job_record(job_id)

            self._finished_jobs[job_hash] = job_id
            self._archived_records[job_id] = job_record

    def store_job_record(self, job_id: uuid.UUID):

        if job_id not in self._archived_records.keys():
            raise Exception(
                f"Can't store job with id '{job_id}': no job record with that id exists."
            )

        job_record = self._archived_records[job_id]

        if job_record._is_stored:
            logger.debug(
                "ignore.store.job_record", reason="already stored", job_id=str(job_id)
            )
            return

        logger.debug(
            "store.job_record",
            job_hash=job_record.job_hash,
            module_type=job_record.module_type,
        )
        store: JobStore = self.get_archive()  # type: ignore
        if not isinstance(store, JobStore):
            raise Exception("Can't store job record to archive: not writable.")

        pre_store_event = JobRecordPreStoreEvent.construct(
            kiara_id=self._kiara.id, job_record=job_record
        )
        self._event_callback(pre_store_event)

        store.store_job_record(job_record)

        stored_event = JobRecordStoredEvent.construct(
            kiara_id=self._kiara.id, job_record=job_record
        )
        self._event_callback(stored_event)

    def get_job_record_in_session(self, job_id: uuid.UUID) -> JobRecord:

        return self._processor.get_job_record(job_id)

    def find_matching_job_record(
        self, inputs_manifest: InputsManifest
    ) -> Optional[uuid.UUID]:
        """Check if a job with same inputs manifest already ran some time before.

        Arguments:
            inputs_manifest: the manifest incl. inputs

        Returns:
            'None' if no such job exists, a (uuid) job-id if the job is currently running or has run in the past
        """

        if inputs_manifest.job_hash in self._active_jobs.keys():
            logger.debug("job.use_running")
            return self._active_jobs[inputs_manifest.job_hash]

        if inputs_manifest.job_hash in self._finished_jobs.keys():
            job_id = self._finished_jobs[inputs_manifest.job_hash]
            return job_id

        matches = []

        for store_id, archive in self._job_archives.items():
            match = archive.find_matching_job_record(inputs_manifest=inputs_manifest)
            if match:
                matches.append(match)

        if len(matches) == 0:
            return None
        elif len(matches) > 1:
            raise Exception(
                f"Multiple stores have a record for inputs manifest '{inputs_manifest}', this is not supported (yet)."
            )

        job_record = matches[0]

        self._finished_jobs[inputs_manifest.job_hash] = job_record.job_id
        self._archived_records[job_record.job_id] = job_record
        logger.debug("job.use_cached")
        return job_record.job_id

    def prepare_job_config(
        self, manifest: Manifest, inputs: Mapping[str, Any]
    ) -> JobConfig:

        module = self._kiara.create_module(manifest=manifest)
        job_config = JobConfig.create_from_module(
            data_registry=self._kiara.data_registry, module=module, inputs=inputs
        )

        return job_config

    def execute(
        self, manifest: Manifest, inputs: Mapping[str, Any], wait: bool = False
    ) -> uuid.UUID:

        job_config = self.prepare_job_config(manifest=manifest, inputs=inputs)
        return self.execute_job(job_config, wait=wait)

    def execute_job(self, job_config: JobConfig, wait: bool = False) -> uuid.UUID:

        log = logger.bind(
            module_type=job_config.module_type,
            module_config=job_config.module_config,
            inputs={k: str(v) for k, v in job_config.inputs.items()},
            job_hash=job_config.job_hash,
        )

        stored_job = self.find_matching_job_record(inputs_manifest=job_config)
        if stored_job is not None:
            return stored_job

        log.debug("job.execute")

        job_id = self._processor.create_job(job_config=job_config)
        self._active_jobs[job_config.job_hash] = job_id

        try:
            self._processor.queue_job(job_id=job_id)
        except Exception as e:
            log.error("error.queue_job", job_id=job_id)
            raise e

        if wait:
            self._processor.wait_for(job_id)

        return job_id

    def get_active_job(self, job_id: uuid.UUID) -> ActiveJob:

        if job_id in self._active_jobs.keys() or job_id in self._failed_jobs.keys():
            return self._processor.get_job(job_id)
        else:
            if job_id in self._archived_records.keys():
                raise Exception(
                    f"Can't retrieve active job with id '{job_id}': job is archived."
                )
            elif job_id in self._processor._failed_jobs.keys():
                job = self._processor.get_job(job_id)
                msg = job.error
                if not msg and job._exception:
                    msg = str(job._exception)
                    if not msg:
                        msg = repr(job._exception)
                raise Exception(f"Job failed: {msg}")
            else:
                raise Exception(f"Can't retrieve job with id '{job_id}': no such job.")

    def get_job_status(self, job_id: uuid.UUID) -> JobStatus:

        if job_id in self._archived_records.keys():
            return JobStatus.SUCCESS
        elif job_id in self._failed_jobs.values():
            return JobStatus.FAILED

        return self._processor.get_job_status(job_id=job_id)

    def wait_for(self, *job_id: uuid.UUID):
        not_finished = (j for j in job_id if j not in self._archived_records.keys())
        if not_finished:
            self._processor.wait_for(*not_finished)

    def retrieve_result(self, job_id: uuid.UUID) -> ValueMap:

        if job_id not in self._archived_records.keys():
            self._processor.wait_for(job_id)

        if job_id in self._archived_records.keys():
            job_record = self._archived_records[job_id]
            results = self._kiara.data_registry.load_values(job_record.outputs)
            return results
        elif job_id in self._failed_jobs.values():
            j = self._processor.get_job(job_id=job_id)
            raise Exception(f"Job failed: {j.error}")
        else:
            raise Exception(f"Could not find job with id: {job_id}")

    def execute_and_retrieve(
        self, manifest: Manifest, inputs: Mapping[str, Any]
    ) -> ValueMap:

        job_id = self.execute(manifest=manifest, inputs=inputs, wait=True)
        results = self.retrieve_result(job_id=job_id)
        return results

default_job_store: str property readonly

Methods

execute(self, manifest, inputs, wait=False)

Source code in kiara/registries/jobs/__init__.py

def execute(
    self, manifest: Manifest, inputs: Mapping[str, Any], wait: bool = False
) -> uuid.UUID:

    job_config = self.prepare_job_config(manifest=manifest, inputs=inputs)
    return self.execute_job(job_config, wait=wait)

execute_and_retrieve(self, manifest, inputs)

Source code in kiara/registries/jobs/__init__.py

def execute_and_retrieve(
    self, manifest: Manifest, inputs: Mapping[str, Any]
) -> ValueMap:

    job_id = self.execute(manifest=manifest, inputs=inputs, wait=True)
    results = self.retrieve_result(job_id=job_id)
    return results

execute_job(self, job_config, wait=False)

Source code in kiara/registries/jobs/__init__.py

def execute_job(self, job_config: JobConfig, wait: bool = False) -> uuid.UUID:

    log = logger.bind(
        module_type=job_config.module_type,
        module_config=job_config.module_config,
        inputs={k: str(v) for k, v in job_config.inputs.items()},
        job_hash=job_config.job_hash,
    )

    stored_job = self.find_matching_job_record(inputs_manifest=job_config)
    if stored_job is not None:
        return stored_job

    log.debug("job.execute")

    job_id = self._processor.create_job(job_config=job_config)
    self._active_jobs[job_config.job_hash] = job_id

    try:
        self._processor.queue_job(job_id=job_id)
    except Exception as e:
        log.error("error.queue_job", job_id=job_id)
        raise e

    if wait:
        self._processor.wait_for(job_id)

    return job_id

find_matching_job_record(self, inputs_manifest)

Check if a job with same inputs manifest already ran some time before.

Parameters:

Name	Type	Description	Default
inputs_manifest	InputsManifest	the manifest incl. inputs	required

Returns:

Type	Description
Optional[uuid.UUID]	'None' if no such job exists, a (uuid) job-id if the job is currently running or has run in the past

Source code in kiara/registries/jobs/__init__.py

def find_matching_job_record(
    self, inputs_manifest: InputsManifest
) -> Optional[uuid.UUID]:
    """Check if a job with same inputs manifest already ran some time before.

    Arguments:
        inputs_manifest: the manifest incl. inputs

    Returns:
        'None' if no such job exists, a (uuid) job-id if the job is currently running or has run in the past
    """

    if inputs_manifest.job_hash in self._active_jobs.keys():
        logger.debug("job.use_running")
        return self._active_jobs[inputs_manifest.job_hash]

    if inputs_manifest.job_hash in self._finished_jobs.keys():
        job_id = self._finished_jobs[inputs_manifest.job_hash]
        return job_id

    matches = []

    for store_id, archive in self._job_archives.items():
        match = archive.find_matching_job_record(inputs_manifest=inputs_manifest)
        if match:
            matches.append(match)

    if len(matches) == 0:
        return None
    elif len(matches) > 1:
        raise Exception(
            f"Multiple stores have a record for inputs manifest '{inputs_manifest}', this is not supported (yet)."
        )

    job_record = matches[0]

    self._finished_jobs[inputs_manifest.job_hash] = job_record.job_id
    self._archived_records[job_record.job_id] = job_record
    logger.debug("job.use_cached")
    return job_record.job_id

get_active_job(self, job_id)

Source code in kiara/registries/jobs/__init__.py

def get_active_job(self, job_id: uuid.UUID) -> ActiveJob:

    if job_id in self._active_jobs.keys() or job_id in self._failed_jobs.keys():
        return self._processor.get_job(job_id)
    else:
        if job_id in self._archived_records.keys():
            raise Exception(
                f"Can't retrieve active job with id '{job_id}': job is archived."
            )
        elif job_id in self._processor._failed_jobs.keys():
            job = self._processor.get_job(job_id)
            msg = job.error
            if not msg and job._exception:
                msg = str(job._exception)
                if not msg:
                    msg = repr(job._exception)
            raise Exception(f"Job failed: {msg}")
        else:
            raise Exception(f"Can't retrieve job with id '{job_id}': no such job.")

get_archive(self, store_id=None)

Source code in kiara/registries/jobs/__init__.py

def get_archive(self, store_id: Optional[str] = None) -> JobArchive:

    if store_id is None:
        store_id = self.default_job_store
        if store_id is None:
            raise Exception("Can't retrieve deafult job archive, none set (yet).")

    return self._job_archives[store_id]

get_job_record_in_session(self, job_id)

Source code in kiara/registries/jobs/__init__.py

def get_job_record_in_session(self, job_id: uuid.UUID) -> JobRecord:

    return self._processor.get_job_record(job_id)

get_job_status(self, job_id)

Source code in kiara/registries/jobs/__init__.py

def get_job_status(self, job_id: uuid.UUID) -> JobStatus:

    if job_id in self._archived_records.keys():
        return JobStatus.SUCCESS
    elif job_id in self._failed_jobs.values():
        return JobStatus.FAILED

    return self._processor.get_job_status(job_id=job_id)

job_status_changed(self, job_id, old_status, new_status)

Source code in kiara/registries/jobs/__init__.py

def job_status_changed(
    self, job_id: uuid.UUID, old_status: Optional[JobStatus], new_status: JobStatus
):

    # print(f"JOB STATUS CHANGED: {job_id} - {old_status} - {new_status.value}")
    if job_id in self._active_jobs.values() and new_status is JobStatus.FAILED:
        job_hash = self._active_jobs.inverse.pop(job_id)
        self._failed_jobs[job_hash] = job_id
    elif job_id in self._active_jobs.values() and new_status is JobStatus.SUCCESS:
        job_hash = self._active_jobs.inverse.pop(job_id)

        job_record = self._processor.get_job_record(job_id)

        self._finished_jobs[job_hash] = job_id
        self._archived_records[job_id] = job_record

prepare_job_config(self, manifest, inputs)

Source code in kiara/registries/jobs/__init__.py

def prepare_job_config(
    self, manifest: Manifest, inputs: Mapping[str, Any]
) -> JobConfig:

    module = self._kiara.create_module(manifest=manifest)
    job_config = JobConfig.create_from_module(
        data_registry=self._kiara.data_registry, module=module, inputs=inputs
    )

    return job_config

register_job_archive(self, archive, alias=None)

Source code in kiara/registries/jobs/__init__.py

def register_job_archive(self, archive: JobArchive, alias: Optional[str] = None):

    if alias is None:
        alias = str(archive.archive_id)

    if alias in self._job_archives.keys():
        raise Exception(
            f"Can't register job store, store id already registered: {alias}."
        )

    self._job_archives[alias] = archive

    is_store = False
    is_default_store = False
    if isinstance(archive, JobStore):
        is_store = True
        if self._default_job_store is None:
            self._default_job_store = alias

    event = JobArchiveAddedEvent.construct(
        kiara_id=self._kiara.id,
        job_archive_id=archive.archive_id,
        job_archive_alias=alias,
        is_store=is_store,
        is_default_store=is_default_store,
    )
    self._event_callback(event)

retrieve_result(self, job_id)

Source code in kiara/registries/jobs/__init__.py

def retrieve_result(self, job_id: uuid.UUID) -> ValueMap:

    if job_id not in self._archived_records.keys():
        self._processor.wait_for(job_id)

    if job_id in self._archived_records.keys():
        job_record = self._archived_records[job_id]
        results = self._kiara.data_registry.load_values(job_record.outputs)
        return results
    elif job_id in self._failed_jobs.values():
        j = self._processor.get_job(job_id=job_id)
        raise Exception(f"Job failed: {j.error}")
    else:
        raise Exception(f"Could not find job with id: {job_id}")

store_job_record(self, job_id)

Source code in kiara/registries/jobs/__init__.py

def store_job_record(self, job_id: uuid.UUID):

    if job_id not in self._archived_records.keys():
        raise Exception(
            f"Can't store job with id '{job_id}': no job record with that id exists."
        )

    job_record = self._archived_records[job_id]

    if job_record._is_stored:
        logger.debug(
            "ignore.store.job_record", reason="already stored", job_id=str(job_id)
        )
        return

    logger.debug(
        "store.job_record",
        job_hash=job_record.job_hash,
        module_type=job_record.module_type,
    )
    store: JobStore = self.get_archive()  # type: ignore
    if not isinstance(store, JobStore):
        raise Exception("Can't store job record to archive: not writable.")

    pre_store_event = JobRecordPreStoreEvent.construct(
        kiara_id=self._kiara.id, job_record=job_record
    )
    self._event_callback(pre_store_event)

    store.store_job_record(job_record)

    stored_event = JobRecordStoredEvent.construct(
        kiara_id=self._kiara.id, job_record=job_record
    )
    self._event_callback(stored_event)

suppoerted_event_types(self)

Source code in kiara/registries/jobs/__init__.py

def suppoerted_event_types(self) -> Iterable[Type[KiaraEvent]]:

    return [JobArchiveAddedEvent, JobRecordPreStoreEvent, JobRecordStoredEvent]

wait_for(self, *job_id)

Source code in kiara/registries/jobs/__init__.py

def wait_for(self, *job_id: uuid.UUID):
    not_finished = (j for j in job_id if j not in self._archived_records.keys())
    if not_finished:
        self._processor.wait_for(*not_finished)

JobStore (JobArchive)

Source code in kiara/registries/jobs/__init__.py

class JobStore(JobArchive):
    @abc.abstractmethod
    def store_job_record(self, job_record: JobRecord):
        pass

store_job_record(self, job_record)

Source code in kiara/registries/jobs/__init__.py

@abc.abstractmethod
def store_job_record(self, job_record: JobRecord):
    pass

Modules

job_store special

Modules

filesystem_store

Classes

FileSystemJobArchive (JobArchive)

Source code in kiara/registries/jobs/job_store/filesystem_store.py

class FileSystemJobArchive(JobArchive):

    _archive_type_name = "filesystem_job_archive"
    _config_cls = FileSystemArchiveConfig

    @classmethod
    def is_writeable(cls) -> bool:
        return False

    @classmethod
    def supported_item_types(cls) -> Iterable[str]:
        return ["job_record"]

    def __init__(self, archive_id: uuid.UUID, config: FileSystemArchiveConfig):

        super().__init__(archive_id=archive_id, config=config)
        self._base_path: Optional[Path] = None

    @property
    def job_store_path(self) -> Path:

        if self._base_path is not None:
            return self._base_path

        self._base_path = Path(self.config.archive_path).absolute()
        self._base_path.mkdir(parents=True, exist_ok=True)
        return self._base_path

    def find_matching_job_record(
        self, inputs_manifest: InputsManifest
    ) -> Optional[JobRecord]:

        manifest_hash = inputs_manifest.instance_cid
        jobs_hash = inputs_manifest.job_hash

        base_path = self.job_store_path / MANIFEST_SUB_PATH
        manifest_folder = base_path / str(manifest_hash)

        if not manifest_folder.exists():
            return None

        manifest_file = manifest_folder / "manifest.json"

        if not manifest_file.exists():
            raise Exception(
                f"No 'manifests.json' file for manifest with hash: {manifest_hash}"
            )

        details_folder = manifest_folder / str(jobs_hash)
        if not details_folder.exists():
            return None

        details_file_name = details_folder / "details.json"
        if not details_file_name.exists():
            raise Exception(
                f"No 'inputs.json' file for manifest/inputs hash-combo: {manifest_hash} / {jobs_hash}"
            )

        details_content = details_file_name.read_text()
        details: Dict[str, Any] = orjson.loads(details_content)

        job_record = JobRecord(**details)
        job_record._is_stored = True
        return job_record

job_store_path: Path property readonly

Classes

_config_cls (ArchiveConfig) private pydantic-model

Source code in kiara/registries/jobs/job_store/filesystem_store.py

class FileSystemArchiveConfig(ArchiveConfig):

    archive_path: str = Field(
        description="The path where the data for this archive is stored."
    )

Attributes

archive_path: str pydantic-field required

The path where the data for this archive is stored.

find_matching_job_record(self, inputs_manifest)

Source code in kiara/registries/jobs/job_store/filesystem_store.py

def find_matching_job_record(
    self, inputs_manifest: InputsManifest
) -> Optional[JobRecord]:

    manifest_hash = inputs_manifest.instance_cid
    jobs_hash = inputs_manifest.job_hash

    base_path = self.job_store_path / MANIFEST_SUB_PATH
    manifest_folder = base_path / str(manifest_hash)

    if not manifest_folder.exists():
        return None

    manifest_file = manifest_folder / "manifest.json"

    if not manifest_file.exists():
        raise Exception(
            f"No 'manifests.json' file for manifest with hash: {manifest_hash}"
        )

    details_folder = manifest_folder / str(jobs_hash)
    if not details_folder.exists():
        return None

    details_file_name = details_folder / "details.json"
    if not details_file_name.exists():
        raise Exception(
            f"No 'inputs.json' file for manifest/inputs hash-combo: {manifest_hash} / {jobs_hash}"
        )

    details_content = details_file_name.read_text()
    details: Dict[str, Any] = orjson.loads(details_content)

    job_record = JobRecord(**details)
    job_record._is_stored = True
    return job_record

is_writeable() classmethod

Source code in kiara/registries/jobs/job_store/filesystem_store.py

@classmethod
def is_writeable(cls) -> bool:
    return False

supported_item_types() classmethod

Source code in kiara/registries/jobs/job_store/filesystem_store.py

@classmethod
def supported_item_types(cls) -> Iterable[str]:
    return ["job_record"]

FileSystemJobStore (FileSystemJobArchive, JobStore)

Source code in kiara/registries/jobs/job_store/filesystem_store.py

class FileSystemJobStore(FileSystemJobArchive, JobStore):

    _archive_type_name = "filesystem_job_store"

    @classmethod
    def is_writeable(cls) -> bool:
        return False

    def store_job_record(self, job_record: JobRecord):

        manifest_hash = job_record.instance_cid
        jobs_hash = job_record.job_hash

        base_path = self.job_store_path / MANIFEST_SUB_PATH
        manifest_folder = base_path / str(manifest_hash)

        manifest_folder.mkdir(parents=True, exist_ok=True)

        manifest_info_file = manifest_folder / "manifest.json"
        if not manifest_info_file.exists():
            manifest_info_file.write_text(job_record.manifest_data_as_json())

        job_folder = manifest_folder / str(jobs_hash)
        job_folder.mkdir(parents=True, exist_ok=True)

        job_details_file_name = job_folder / "details.json"
        if job_details_file_name.exists():
            raise Exception(
                f"Job record already exists: {job_details_file_name.as_posix()}"
            )

        job_details_file_name.write_text(job_record.json())

        for output_name, output_v_id in job_record.outputs.items():

            outputs_file_name = (
                job_folder / f"output__{output_name}__value_id__{output_v_id}.json"
            )

            if outputs_file_name.exists():
                # if value.pedigree_output_name == "__void__":
                #     return
                # else:
                raise Exception(f"Can't write value '{output_v_id}': already exists.")
            else:
                outputs_file_name.touch()

is_writeable() classmethod

Source code in kiara/registries/jobs/job_store/filesystem_store.py

@classmethod
def is_writeable(cls) -> bool:
    return False

store_job_record(self, job_record)

Source code in kiara/registries/jobs/job_store/filesystem_store.py

def store_job_record(self, job_record: JobRecord):

    manifest_hash = job_record.instance_cid
    jobs_hash = job_record.job_hash

    base_path = self.job_store_path / MANIFEST_SUB_PATH
    manifest_folder = base_path / str(manifest_hash)

    manifest_folder.mkdir(parents=True, exist_ok=True)

    manifest_info_file = manifest_folder / "manifest.json"
    if not manifest_info_file.exists():
        manifest_info_file.write_text(job_record.manifest_data_as_json())

    job_folder = manifest_folder / str(jobs_hash)
    job_folder.mkdir(parents=True, exist_ok=True)

    job_details_file_name = job_folder / "details.json"
    if job_details_file_name.exists():
        raise Exception(
            f"Job record already exists: {job_details_file_name.as_posix()}"
        )

    job_details_file_name.write_text(job_record.json())

    for output_name, output_v_id in job_record.outputs.items():

        outputs_file_name = (
            job_folder / f"output__{output_name}__value_id__{output_v_id}.json"
        )

        if outputs_file_name.exists():
            # if value.pedigree_output_name == "__void__":
            #     return
            # else:
            raise Exception(f"Can't write value '{output_v_id}': already exists.")
        else:
            outputs_file_name.touch()

models special

Classes

ModelRegistry

Source code in kiara/registries/models/__init__.py

class ModelRegistry(object):

    _instance = None

    @classmethod
    def instance(cls) -> "ModelRegistry":
        """The default *kiara* context. In most cases, it's recommended you create and manage your own, though."""

        if cls._instance is None:
            cls._instance = ModelRegistry()
        return cls._instance

    def __init__(self):

        self._all_models: Optional[KiaraModelClassesInfo] = None
        self._models_per_package: Dict[str, KiaraModelClassesInfo] = {}
        self._sub_models: Dict[Type[KiaraModel], KiaraModelClassesInfo] = {}

    @property
    def all_models(self) -> KiaraModelClassesInfo:

        if self._all_models is not None:
            return self._all_models

        self._all_models = find_kiara_models()
        return self._all_models

    def get_model_cls(
        self, kiara_model_id: str, required_subclass: Optional[Type[KiaraModel]] = None
    ) -> Type[KiaraModel]:

        model_info = self.all_models.get(kiara_model_id, None)
        if model_info is None:
            raise Exception(
                f"Can't retrieve model class for id '{kiara_model_id}': id not registered."
            )

        cls = model_info.python_class.get_class()  # type: ignore
        if required_subclass:
            if not issubclass(cls, required_subclass):
                raise Exception(
                    f"Can't retrieve sub model of '{required_subclass.__name__}' with id '{kiara_model_id}': exists, but not the required subclass."
                )

        return cls  # type: ignore

    def get_models_for_package(self, package_name: str) -> KiaraModelClassesInfo:

        if package_name in self._models_per_package.keys():
            return self._models_per_package[package_name]

        temp = {}
        for key, info in self.all_models.items():
            if info.context.labels.get("package") == package_name:
                temp[key] = info

        group = KiaraModelClassesInfo.construct(
            group_alias=f"kiara_models.{package_name}", type_infos=temp  # type: ignore
        )

        self._models_per_package[package_name] = group
        return group

    def get_models_of_type(self, model_type: Type[KiaraModel]) -> KiaraModelClassesInfo:

        if model_type in self._sub_models.keys():
            return self._sub_models[model_type]

        sub_classes = {}
        for model_id, type_info in self.all_models.type_infos.items():
            cls: Type[KiaraModel] = type_info.python_class.get_class()  # type: ignore

            if issubclass(cls, model_type):
                sub_classes[model_id] = type_info

        classes = KiaraModelClassesInfo(
            group_alias=f"{model_type.__name__}-submodels", type_infos=sub_classes
        )
        self._sub_models[model_type] = classes
        return classes

all_models: KiaraModelClassesInfo property readonly

Methods

get_model_cls(self, kiara_model_id, required_subclass=None)

Source code in kiara/registries/models/__init__.py

def get_model_cls(
    self, kiara_model_id: str, required_subclass: Optional[Type[KiaraModel]] = None
) -> Type[KiaraModel]:

    model_info = self.all_models.get(kiara_model_id, None)
    if model_info is None:
        raise Exception(
            f"Can't retrieve model class for id '{kiara_model_id}': id not registered."
        )

    cls = model_info.python_class.get_class()  # type: ignore
    if required_subclass:
        if not issubclass(cls, required_subclass):
            raise Exception(
                f"Can't retrieve sub model of '{required_subclass.__name__}' with id '{kiara_model_id}': exists, but not the required subclass."
            )

    return cls  # type: ignore

get_models_for_package(self, package_name)

Source code in kiara/registries/models/__init__.py

def get_models_for_package(self, package_name: str) -> KiaraModelClassesInfo:

    if package_name in self._models_per_package.keys():
        return self._models_per_package[package_name]

    temp = {}
    for key, info in self.all_models.items():
        if info.context.labels.get("package") == package_name:
            temp[key] = info

    group = KiaraModelClassesInfo.construct(
        group_alias=f"kiara_models.{package_name}", type_infos=temp  # type: ignore
    )

    self._models_per_package[package_name] = group
    return group

get_models_of_type(self, model_type)

Source code in kiara/registries/models/__init__.py

def get_models_of_type(self, model_type: Type[KiaraModel]) -> KiaraModelClassesInfo:

    if model_type in self._sub_models.keys():
        return self._sub_models[model_type]

    sub_classes = {}
    for model_id, type_info in self.all_models.type_infos.items():
        cls: Type[KiaraModel] = type_info.python_class.get_class()  # type: ignore

        if issubclass(cls, model_type):
            sub_classes[model_id] = type_info

    classes = KiaraModelClassesInfo(
        group_alias=f"{model_type.__name__}-submodels", type_infos=sub_classes
    )
    self._sub_models[model_type] = classes
    return classes

instance() classmethod

The default kiara context. In most cases, it's recommended you create and manage your own, though.

Source code in kiara/registries/models/__init__.py

@classmethod
def instance(cls) -> "ModelRegistry":
    """The default *kiara* context. In most cases, it's recommended you create and manage your own, though."""

    if cls._instance is None:
        cls._instance = ModelRegistry()
    return cls._instance

modules special

Base module for code that handles the import and management of [KiaraModule][kiara.module.KiaraModule] sub-classes.

logget

Classes

ModuleRegistry

Source code in kiara/registries/modules/__init__.py

class ModuleRegistry(object):
    def __init__(self):

        self._cached_modules: Dict[str, Dict[CID, KiaraModule]] = {}

        from kiara.utils.class_loading import find_all_kiara_modules

        module_classes = find_all_kiara_modules()

        self._module_classes: Mapping[str, Type[KiaraModule]] = {}
        self._module_class_metadata: Dict[str, KiaraModuleTypeInfo] = {}

        for k, v in module_classes.items():
            self._module_classes[k] = v

    @property
    def module_types(self) -> Mapping[str, Type["KiaraModule"]]:
        return self._module_classes

    def get_module_class(self, module_type: str) -> Type["KiaraModule"]:

        cls = self._module_classes.get(module_type, None)
        if cls is None:
            raise ValueError(f"No module of type '{module_type}' available.")
        return cls

    def get_module_type_names(self) -> Iterable[str]:
        return self._module_classes.keys()

    def get_module_type_metadata(self, type_name: str) -> KiaraModuleTypeInfo:

        md = self._module_class_metadata.get(type_name, None)
        if md is None:
            md = KiaraModuleTypeInfo.create_from_type_class(
                self.get_module_class(module_type=type_name)
            )
            self._module_class_metadata[type_name] = md
        return self._module_class_metadata[type_name]

    def get_context_metadata(
        self, alias: Optional[str] = None, only_for_package: Optional[str] = None
    ) -> ModuleTypeClassesInfo:

        result = {}
        for type_name in self.module_types.keys():
            md = self.get_module_type_metadata(type_name=type_name)
            if only_for_package:
                if md.context.labels.get("package") == only_for_package:
                    result[type_name] = md
            else:
                result[type_name] = md

        return ModuleTypeClassesInfo.construct(group_alias=alias, type_infos=result)  # type: ignore

    def create_module(self, manifest: Union[Manifest, str]) -> "KiaraModule":
        """Create a [KiaraModule][kiara.module.KiaraModule] object from a module configuration.

        Arguments:
            manifest: the module configuration
        """

        if isinstance(manifest, str):
            manifest = Manifest.construct(module_type=manifest, module_config={})

        if self._cached_modules.setdefault(manifest.module_type, {}).get(
            manifest.instance_cid, None
        ):
            return self._cached_modules[manifest.module_type][manifest.instance_cid]

        if manifest.module_type in self.get_module_type_names():

            m_cls: Type[KiaraModule] = self.get_module_class(manifest.module_type)
            m_hash = m_cls._calculate_module_cid(manifest.module_config)

            kiara_module = m_cls(module_config=manifest.module_config)
            assert (
                kiara_module.module_instance_cid == m_hash
            )  # TODO: might not be necessary? Leaving it in here for now, to see if it triggers at any stage.
        else:
            raise Exception(
                f"Invalid module type '{manifest.module_type}'. Available type names: {', '.join(self.get_module_type_names())}"
            )

        return kiara_module

module_types: Mapping[str, Type[KiaraModule]] property readonly

Methods

create_module(self, manifest)

Create a [KiaraModule][kiara.module.KiaraModule] object from a module configuration.

Parameters:

Name	Type	Description	Default
manifest	Union[kiara.models.module.manifest.Manifest, str]	the module configuration	required

Source code in kiara/registries/modules/__init__.py

def create_module(self, manifest: Union[Manifest, str]) -> "KiaraModule":
    """Create a [KiaraModule][kiara.module.KiaraModule] object from a module configuration.

    Arguments:
        manifest: the module configuration
    """

    if isinstance(manifest, str):
        manifest = Manifest.construct(module_type=manifest, module_config={})

    if self._cached_modules.setdefault(manifest.module_type, {}).get(
        manifest.instance_cid, None
    ):
        return self._cached_modules[manifest.module_type][manifest.instance_cid]

    if manifest.module_type in self.get_module_type_names():

        m_cls: Type[KiaraModule] = self.get_module_class(manifest.module_type)
        m_hash = m_cls._calculate_module_cid(manifest.module_config)

        kiara_module = m_cls(module_config=manifest.module_config)
        assert (
            kiara_module.module_instance_cid == m_hash
        )  # TODO: might not be necessary? Leaving it in here for now, to see if it triggers at any stage.
    else:
        raise Exception(
            f"Invalid module type '{manifest.module_type}'. Available type names: {', '.join(self.get_module_type_names())}"
        )

    return kiara_module

get_context_metadata(self, alias=None, only_for_package=None)

Source code in kiara/registries/modules/__init__.py

def get_context_metadata(
    self, alias: Optional[str] = None, only_for_package: Optional[str] = None
) -> ModuleTypeClassesInfo:

    result = {}
    for type_name in self.module_types.keys():
        md = self.get_module_type_metadata(type_name=type_name)
        if only_for_package:
            if md.context.labels.get("package") == only_for_package:
                result[type_name] = md
        else:
            result[type_name] = md

    return ModuleTypeClassesInfo.construct(group_alias=alias, type_infos=result)  # type: ignore

get_module_class(self, module_type)

Source code in kiara/registries/modules/__init__.py

def get_module_class(self, module_type: str) -> Type["KiaraModule"]:

    cls = self._module_classes.get(module_type, None)
    if cls is None:
        raise ValueError(f"No module of type '{module_type}' available.")
    return cls

get_module_type_metadata(self, type_name)

Source code in kiara/registries/modules/__init__.py

def get_module_type_metadata(self, type_name: str) -> KiaraModuleTypeInfo:

    md = self._module_class_metadata.get(type_name, None)
    if md is None:
        md = KiaraModuleTypeInfo.create_from_type_class(
            self.get_module_class(module_type=type_name)
        )
        self._module_class_metadata[type_name] = md
    return self._module_class_metadata[type_name]

get_module_type_names(self)

Source code in kiara/registries/modules/__init__.py

def get_module_type_names(self) -> Iterable[str]:
    return self._module_classes.keys()

operations special

logger

OperationRegistry

Source code in kiara/registries/operations/__init__.py

class OperationRegistry(object):
    def __init__(
        self,
        kiara: "Kiara",
        operation_type_classes: Optional[Mapping[str, Type[OperationType]]] = None,
    ):

        self._kiara: "Kiara" = kiara

        self._operation_type_classes: Optional[Dict[str, Type["OperationType"]]] = None

        if operation_type_classes is not None:
            self._operation_type_classes = dict(operation_type_classes)

        self._operation_type_metadata: Dict[str, OperationTypeInfo] = {}

        self._operation_types: Optional[Dict[str, OperationType]] = None

        self._operations: Optional[Dict[str, Operation]] = None
        self._operations_by_type: Optional[Dict[str, Iterable[str]]] = None

    @property
    def is_initialized(self) -> bool:

        return self._operations is not None

    @property
    def operation_types(self) -> Mapping[str, OperationType]:

        if self._operation_types is not None:
            return self._operation_types

        # TODO: support op type config
        _operation_types = {}
        for op_name, op_cls in self.operation_type_classes.items():
            try:
                _operation_types[op_name] = op_cls(
                    kiara=self._kiara, op_type_name=op_name
                )
            except Exception as e:
                if is_debug():
                    import traceback

                    traceback.print_exc()
                logger.debug("ignore.operation_type", operation_name=op_name, reason=e)

        self._operation_types = _operation_types
        return self._operation_types

    def get_operation_type(self, op_type: str) -> OperationType:

        if op_type not in self.operation_types.keys():
            raise Exception(
                f"No operation type '{op_type}' registered. Available operation types: {', '.join(self.operation_types.keys())}."
            )

        return self.operation_types[op_type]

    def get_type_metadata(self, type_name: str) -> OperationTypeInfo:

        md = self._operation_type_metadata.get(type_name, None)
        if md is None:
            md = OperationTypeInfo.create_from_type_class(
                type_cls=self.operation_type_classes[type_name]
            )
            self._operation_type_metadata[type_name] = md
        return self._operation_type_metadata[type_name]

    def get_context_metadata(
        self, alias: Optional[str] = None, only_for_package: Optional[str] = None
    ) -> OperationTypeClassesInfo:

        result = {}
        for type_name in self.operation_type_classes.keys():
            md = self.get_type_metadata(type_name=type_name)
            if only_for_package:
                if md.context.labels.get("package") == only_for_package:
                    result[type_name] = md
            else:
                result[type_name] = md

        return OperationTypeClassesInfo.construct(group_alias=alias, type_infos=result)  # type: ignore

    @property
    def operation_type_classes(
        self,
    ) -> Mapping[str, Type["OperationType"]]:

        if self._operation_type_classes is not None:
            return self._operation_type_classes

        from kiara.utils.class_loading import find_all_operation_types

        self._operation_type_classes = find_all_operation_types()
        return self._operation_type_classes

    # @property
    # def operation_ids(self) -> List[str]:
    #     return list(self.profiles.keys())

    @property
    def operation_ids(self) -> Iterable[str]:
        return self.operations.keys()

    @property
    def operations(self) -> Mapping[str, Operation]:

        if self._operations is not None:
            return self._operations

        all_op_configs: Set[OperationConfig] = set()
        for op_type in self.operation_types.values():
            included_ops = op_type.retrieve_included_operation_configs()
            for op in included_ops:
                if isinstance(op, Mapping):
                    op = ManifestOperationConfig(**op)
                all_op_configs.add(op)

        for data_type in self._kiara.data_type_classes.values():
            if hasattr(data_type, "retrieve_included_operations"):
                for op in all_op_configs:
                    if isinstance(op, Mapping):
                        op = ManifestOperationConfig(**op)
                    all_op_configs.add(op)

        operations: Dict[str, Operation] = {}
        operations_by_type: Dict[str, List[str]] = {}

        deferred_module_names: Dict[str, List[OperationConfig]] = {}

        # first iteration
        for op_config in all_op_configs:

            try:

                if isinstance(op_config, PipelineOperationConfig):
                    for mt in op_config.required_module_types:
                        if mt not in self._kiara.module_type_names:
                            deferred_module_names.setdefault(mt, []).append(op_config)
                    deferred_module_names.setdefault(
                        op_config.pipeline_name, []
                    ).append(op_config)
                    continue

                module_type = op_config.retrieve_module_type(kiara=self._kiara)
                if module_type not in self._kiara.module_type_names:
                    deferred_module_names.setdefault(module_type, []).append(op_config)
                else:
                    module_config = op_config.retrieve_module_config(kiara=self._kiara)

                    manifest = Manifest.construct(
                        module_type=module_type, module_config=module_config
                    )

                    ops = self._create_operations(manifest=manifest, doc=op_config.doc)

                    for op_type_name, _op in ops.items():
                        if _op.operation_id in operations.keys():
                            logger.debug(
                                "duplicate_operation_id",
                                op_id=_op.operation_id,
                                left_module=operations[_op.operation_id].module_type,
                                right_module=_op.module_type,
                            )
                            raise Exception(
                                f"Duplicate operation id: {_op.operation_id}"
                            )
                        operations[_op.operation_id] = _op
                        operations_by_type.setdefault(op_type_name, []).append(
                            _op.operation_id
                        )
            except Exception as e:
                details: Dict[str, Any] = {}
                module_id = op_config.retrieve_module_type(kiara=self._kiara)
                details["module_id"] = module_id
                if module_id == "pipeline":
                    details["pipeline_name"] = op_config.pipeline_name  # type: ignore
                msg: Union[str, Exception] = str(e)
                if not msg:
                    msg = e
                details["details"] = msg
                logger.error("invalid.operation", **details)
                if is_debug():
                    import traceback

                    traceback.print_exc()

                continue

        error_details = {}
        while deferred_module_names:

            deferred_length = len(deferred_module_names)

            remove_deferred_names = set()

            for missing_op_id in deferred_module_names.keys():
                if missing_op_id in operations.keys():
                    remove_deferred_names.add(missing_op_id)
                    continue

                for op_config in deferred_module_names[missing_op_id]:
                    try:

                        if isinstance(op_config, PipelineOperationConfig):

                            if all(
                                mt in self._kiara.module_type_names
                                or mt in operations.keys()
                                for mt in op_config.required_module_types
                            ):
                                module_map = {}
                                for mt in op_config.required_module_types:
                                    if mt in operations.keys():
                                        module_map[mt] = {
                                            "module_type": operations[mt].module_type,
                                            "module_config": operations[
                                                mt
                                            ].module_config,
                                        }
                                op_config.module_map.update(module_map)
                                module_config = op_config.retrieve_module_config(
                                    kiara=self._kiara
                                )

                                manifest = Manifest.construct(
                                    module_type="pipeline",
                                    module_config=module_config,
                                )
                                ops = self._create_operations(
                                    manifest=manifest,
                                    doc=op_config.doc,
                                    metadata=op_config.metadata,
                                )

                            else:
                                missing = (
                                    mt
                                    for mt in op_config.required_module_types
                                    if mt not in self._kiara.module_type_names
                                    and mt not in operations.keys()
                                )
                                raise Exception(
                                    f"Can't find all required module types when processing pipeline '{missing_op_id}': {', '.join(missing)}"
                                )

                        else:
                            raise NotImplementedError(
                                f"Invalid type: {type(op_config)}"
                            )
                            # module_type = op_config.retrieve_module_type(kiara=self._kiara)
                            # module_config = op_config.retrieve_module_config(kiara=self._kiara)
                            #
                            # # TODO: merge dicts instead of update?
                            # new_module_config = dict(base_config)
                            # new_module_config.update(module_config)
                            #
                            # manifest = Manifest.construct(module_type=operation.module_type,
                            #                       module_config=new_module_config)

                        for op_type_name, _op in ops.items():

                            if _op.operation_id in operations.keys():
                                raise Exception(
                                    f"Duplicate operation id: {_op.operation_id}"
                                )

                            operations[_op.operation_id] = _op
                            operations_by_type.setdefault(op_type_name, []).append(
                                _op.operation_id
                            )
                            assert _op.operation_id == op_config.pipeline_name

                        for _op_id in deferred_module_names.keys():
                            if op_config in deferred_module_names[_op_id]:
                                deferred_module_names[_op_id].remove(op_config)
                    except Exception as e:
                        details = {}
                        module_id = op_config.retrieve_module_type(kiara=self._kiara)
                        details["module_id"] = module_id
                        if module_id == "pipeline":
                            details["pipeline_name"] = op_config.pipeline_name  # type: ignore
                        msg = str(e)
                        if not msg:
                            msg = e
                        details["details"] = msg
                        error_details[missing_op_id] = details
                        exc_info = sys.exc_info()
                        details["exception"] = exc_info
                        continue

            for name, dependencies in deferred_module_names.items():
                if not dependencies:
                    remove_deferred_names.add(name)

            for rdn in remove_deferred_names:
                deferred_module_names.pop(rdn)

            if len(deferred_module_names) == deferred_length:
                for mn in deferred_module_names:
                    if mn in operations.keys():
                        continue
                    details = error_details.get(missing_op_id, {"details": "-- n/a --"})
                    exception = details.pop("exception", None)
                    if exception and is_debug():
                        import traceback

                        traceback.print_exception(*exception)

                    logger.error(f"invalid.operation.{mn}", operation_id=mn, **details)
                break

        self._operations = {}
        for missing_op_id in sorted(operations.keys()):
            self._operations[missing_op_id] = operations[missing_op_id]

        self._operations_by_type = {}
        for op_type_name in sorted(operations_by_type.keys()):
            self._operations_by_type.setdefault(
                op_type_name, sorted(operations_by_type[op_type_name])
            )

        return self._operations

    def _create_operations(
        self, manifest: Manifest, doc: Any, metadata: Optional[Mapping[str, Any]] = None
    ) -> Dict[str, Operation]:

        module = self._kiara.create_module(manifest)
        op_types = {}

        if metadata is None:
            metadata = {}

        for op_name, op_type in self.operation_types.items():

            op_details = op_type.check_matching_operation(module=module)
            if not op_details:
                continue

            operation = Operation(
                module_type=manifest.module_type,
                module_config=manifest.module_config,
                operation_id=op_details.operation_id,
                operation_details=op_details,
                module_details=KiaraModuleClass.from_module(module),
                metadata=metadata,
                doc=doc,
            )
            operation._module = module

            op_types[op_name] = operation

        return op_types

    def get_operation(self, operation_id: str) -> Operation:

        if operation_id not in self.operation_ids:
            raise Exception(f"No operation registered with id: {operation_id}")

        op = self.operations[operation_id]
        return op

    def find_all_operation_types(self, operation_id: str) -> Set[str]:

        result = set()
        for op_type, ops in self.operations_by_type.items():
            if operation_id in ops:
                result.add(op_type)

        return result

    @property
    def operations_by_type(self) -> Mapping[str, Iterable[str]]:

        if self._operations_by_type is None:
            self.operations  # noqa
        return self._operations_by_type  # type: ignore

is_initialized: bool property readonly

operation_ids: Iterable[str] property readonly

operation_type_classes: Mapping[str, Type[OperationType]] property readonly

operation_types: Mapping[str, kiara.operations.OperationType] property readonly

operations: Mapping[str, kiara.models.module.operation.Operation] property readonly

operations_by_type: Mapping[str, Iterable[str]] property readonly

find_all_operation_types(self, operation_id)

Source code in kiara/registries/operations/__init__.py

def find_all_operation_types(self, operation_id: str) -> Set[str]:

    result = set()
    for op_type, ops in self.operations_by_type.items():
        if operation_id in ops:
            result.add(op_type)

    return result

get_context_metadata(self, alias=None, only_for_package=None)

Source code in kiara/registries/operations/__init__.py

def get_context_metadata(
    self, alias: Optional[str] = None, only_for_package: Optional[str] = None
) -> OperationTypeClassesInfo:

    result = {}
    for type_name in self.operation_type_classes.keys():
        md = self.get_type_metadata(type_name=type_name)
        if only_for_package:
            if md.context.labels.get("package") == only_for_package:
                result[type_name] = md
        else:
            result[type_name] = md

    return OperationTypeClassesInfo.construct(group_alias=alias, type_infos=result)  # type: ignore

get_operation(self, operation_id)

Source code in kiara/registries/operations/__init__.py

def get_operation(self, operation_id: str) -> Operation:

    if operation_id not in self.operation_ids:
        raise Exception(f"No operation registered with id: {operation_id}")

    op = self.operations[operation_id]
    return op

get_operation_type(self, op_type)

Source code in kiara/registries/operations/__init__.py

def get_operation_type(self, op_type: str) -> OperationType:

    if op_type not in self.operation_types.keys():
        raise Exception(
            f"No operation type '{op_type}' registered. Available operation types: {', '.join(self.operation_types.keys())}."
        )

    return self.operation_types[op_type]

get_type_metadata(self, type_name)

Source code in kiara/registries/operations/__init__.py

def get_type_metadata(self, type_name: str) -> OperationTypeInfo:

    md = self._operation_type_metadata.get(type_name, None)
    if md is None:
        md = OperationTypeInfo.create_from_type_class(
            type_cls=self.operation_type_classes[type_name]
        )
        self._operation_type_metadata[type_name] = md
    return self._operation_type_metadata[type_name]

types special

TYPE_PROFILE_MAP

Classes

TypeRegistry

Source code in kiara/registries/types/__init__.py

class TypeRegistry(object):
    def __init__(self, kiara: "Kiara"):

        self._kiara: Kiara = kiara
        self._data_types: Optional[bidict[str, Type[DataType]]] = None
        self._data_type_metadata: Dict[str, DataTypeClassInfo] = {}
        self._cached_data_type_objects: Dict[int, DataType] = {}
        # self._registered_python_classes: Dict[Type, typing.List[str]] = None  # type: ignore
        self._type_hierarchy: Optional[nx.DiGraph] = None
        self._lineages_cache: Dict[str, List[str]] = {}

        self._type_profiles: Optional[Dict[str, Mapping[str, Any]]] = None

    def invalidate_types(self):

        self._data_types = None
        # self._registered_python_classes = None

    def retrieve_data_type(
        self, data_type_name: str, data_type_config: Optional[Mapping[str, Any]] = None
    ) -> DataType:

        if data_type_config is None:
            data_type_config = {}
        else:
            data_type_config = dict(data_type_config)

        if data_type_name not in self.data_type_profiles.keys():
            raise Exception(f"Data type name not registered: {data_type_name}")

        data_type: str = self.data_type_profiles[data_type_name]["type_name"]
        type_config = self.data_type_profiles[data_type_name]["type_config"]

        if data_type_config:
            type_config = dict(type_config)
            type_config.update(data_type_config)

        cls = self.get_data_type_cls(type_name=data_type)

        hash = cls._calculate_data_type_hash(type_config)
        if hash in self._cached_data_type_objects.keys():
            return self._cached_data_type_objects[hash]

        result = cls(**type_config)
        assert result.data_type_hash == hash
        self._cached_data_type_objects[result.data_type_hash] = result
        return result

    @property
    def data_type_classes(self) -> bidict[str, Type[DataType]]:

        if self._data_types is not None:
            return self._data_types

        self._data_types = bidict(find_all_data_types())
        profiles: Dict[str, Mapping[str, Any]] = {
            dn: {"type_name": dn, "type_config": {}} for dn in self._data_types.keys()
        }

        for name, cls in self._data_types.items():
            cls_profiles = cls.retrieve_available_type_profiles()
            for profile_name, type_config in cls_profiles.items():
                if profile_name in profiles.keys():
                    raise Exception(f"Duplicate data type profile: {profile_name}")
                profiles[profile_name] = {"type_name": name, "type_config": type_config}

        self._type_profiles = profiles
        return self._data_types

    @property
    def data_type_profiles(self) -> Mapping[str, Mapping[str, Any]]:

        if self._type_profiles is None:
            self.data_type_classes  # noqa
        assert self._type_profiles is not None
        return self._type_profiles

    @property
    def data_type_hierarchy(self) -> "nx.DiGraph":

        if self._type_hierarchy is not None:
            return self._type_hierarchy

        def recursive_base_find(cls: Type, current: Optional[List[str]] = None):

            if current is None:
                current = []

            for base in cls.__bases__:

                if base in self.data_type_classes.values():
                    current.append(self.data_type_classes.inverse[base])

                recursive_base_find(base, current=current)

            return current

        bases = {}
        for name, cls in self.data_type_classes.items():
            bases[name] = recursive_base_find(cls)

        for profile_name, details in self.data_type_profiles.items():

            if not details["type_config"]:
                continue
            if profile_name in bases.keys():
                raise Exception(
                    f"Invalid profile name '{profile_name}': shadowing data type. This is most likely a bug."
                )
            bases[profile_name] = [details["type_name"]]

        import networkx as nx

        hierarchy = nx.DiGraph()
        hierarchy.add_node(KIARA_ROOT_TYPE_NAME)

        for name, _bases in bases.items():
            profile_details = self.data_type_profiles[name]
            cls = self.data_type_classes[profile_details["type_name"]]
            hierarchy.add_node(name, cls=cls)
            if not _bases:
                hierarchy.add_edge(KIARA_ROOT_TYPE_NAME, name)
            else:
                # we only need the first parent, all others will be taken care of by the parent of the parent
                hierarchy.add_edge(_bases[0], name)

        self._type_hierarchy = hierarchy
        return self._type_hierarchy

    def get_sub_hierarchy(self, data_type: str):

        import networkx as nx

        graph: nx.DiGraph = self.data_type_hierarchy

        desc = nx.descendants(graph, data_type)
        desc.add(data_type)
        sub_graph = graph.subgraph(desc)
        return sub_graph

    def get_type_lineage(self, data_type_name: str) -> Iterable[str]:
        """Returns the shortest path between the specified type and the root, in reverse direction starting from the specified type."""

        if data_type_name not in self.data_type_profiles.keys():
            raise Exception(f"No data type '{data_type_name}' registered.")

        if data_type_name in self._lineages_cache.keys():
            return self._lineages_cache[data_type_name]

        import networkx as nx

        path = nx.shortest_path(
            self.data_type_hierarchy, KIARA_ROOT_TYPE_NAME, data_type_name
        )
        path.remove(KIARA_ROOT_TYPE_NAME)
        self._lineages_cache[data_type_name] = list(reversed(path))
        return self._lineages_cache[data_type_name]

    def get_sub_types(self, data_type_name: str) -> Set[str]:

        if data_type_name not in self.data_type_classes.keys():
            raise Exception(f"No data type '{data_type_name}' registered.")

        import networkx as nx

        desc = nx.descendants(self.data_type_hierarchy, data_type_name)
        return desc

    def get_associated_profiles(
        self, data_type_name: str
    ) -> Mapping[str, Mapping[str, Any]]:

        if data_type_name not in self.data_type_classes.keys():
            raise Exception(f"No data type '{data_type_name}' registered.")

        result = {}
        for profile_name, details in self.data_type_profiles.items():
            if (
                profile_name != data_type_name
                and data_type_name == details["type_name"]
            ):
                result[profile_name] = details

        return result

    @property
    def data_type_names(self) -> List[str]:
        return list(self.data_type_profiles.keys())

    def get_data_type_cls(self, type_name: str) -> Type[DataType]:

        _type_details = self.data_type_profiles.get(type_name, None)
        if _type_details is None:
            raise Exception(
                f"No value type '{type_name}', available types: {', '.join(self.data_type_profiles.keys())}"
            )

        resolved_type_name: str = _type_details["type_name"]

        t = self.data_type_classes.get(resolved_type_name, None)
        if t is None:
            raise Exception(
                f"No value type '{type_name}', available types: {', '.join(self.data_type_profiles.keys())}"
            )
        return t

    def get_type_metadata(self, type_name: str) -> DataTypeClassInfo:

        md = self._data_type_metadata.get(type_name, None)
        if md is None:
            md = DataTypeClassInfo.create_from_type_class(
                type_cls=self.get_data_type_cls(type_name=type_name), kiara=self._kiara
            )
            self._data_type_metadata[type_name] = md
        return self._data_type_metadata[type_name]

    def get_context_metadata(
        self, alias: Optional[str] = None, only_for_package: Optional[str] = None
    ) -> DataTypeClassesInfo:

        result = {}
        for type_name in self.data_type_classes.keys():
            md = self.get_type_metadata(type_name=type_name)
            if only_for_package:
                if md.context.labels.get("package") == only_for_package:
                    result[type_name] = md
            else:
                result[type_name] = md

        _result = DataTypeClassesInfo.construct(group_alias=alias, type_infos=result)  # type: ignore
        _result._kiara = self._kiara
        return _result

data_type_classes: bidict property readonly

data_type_hierarchy: nx.DiGraph property readonly

data_type_names: List[str] property readonly

data_type_profiles: Mapping[str, Mapping[str, Any]] property readonly

Methods

get_associated_profiles(self, data_type_name)

Source code in kiara/registries/types/__init__.py

def get_associated_profiles(
    self, data_type_name: str
) -> Mapping[str, Mapping[str, Any]]:

    if data_type_name not in self.data_type_classes.keys():
        raise Exception(f"No data type '{data_type_name}' registered.")

    result = {}
    for profile_name, details in self.data_type_profiles.items():
        if (
            profile_name != data_type_name
            and data_type_name == details["type_name"]
        ):
            result[profile_name] = details

    return result

get_context_metadata(self, alias=None, only_for_package=None)

Source code in kiara/registries/types/__init__.py

def get_context_metadata(
    self, alias: Optional[str] = None, only_for_package: Optional[str] = None
) -> DataTypeClassesInfo:

    result = {}
    for type_name in self.data_type_classes.keys():
        md = self.get_type_metadata(type_name=type_name)
        if only_for_package:
            if md.context.labels.get("package") == only_for_package:
                result[type_name] = md
        else:
            result[type_name] = md

    _result = DataTypeClassesInfo.construct(group_alias=alias, type_infos=result)  # type: ignore
    _result._kiara = self._kiara
    return _result

get_data_type_cls(self, type_name)

Source code in kiara/registries/types/__init__.py

def get_data_type_cls(self, type_name: str) -> Type[DataType]:

    _type_details = self.data_type_profiles.get(type_name, None)
    if _type_details is None:
        raise Exception(
            f"No value type '{type_name}', available types: {', '.join(self.data_type_profiles.keys())}"
        )

    resolved_type_name: str = _type_details["type_name"]

    t = self.data_type_classes.get(resolved_type_name, None)
    if t is None:
        raise Exception(
            f"No value type '{type_name}', available types: {', '.join(self.data_type_profiles.keys())}"
        )
    return t

get_sub_hierarchy(self, data_type)

Source code in kiara/registries/types/__init__.py

def get_sub_hierarchy(self, data_type: str):

    import networkx as nx

    graph: nx.DiGraph = self.data_type_hierarchy

    desc = nx.descendants(graph, data_type)
    desc.add(data_type)
    sub_graph = graph.subgraph(desc)
    return sub_graph

get_sub_types(self, data_type_name)

Source code in kiara/registries/types/__init__.py

def get_sub_types(self, data_type_name: str) -> Set[str]:

    if data_type_name not in self.data_type_classes.keys():
        raise Exception(f"No data type '{data_type_name}' registered.")

    import networkx as nx

    desc = nx.descendants(self.data_type_hierarchy, data_type_name)
    return desc

get_type_lineage(self, data_type_name)

Returns the shortest path between the specified type and the root, in reverse direction starting from the specified type.

Source code in kiara/registries/types/__init__.py

def get_type_lineage(self, data_type_name: str) -> Iterable[str]:
    """Returns the shortest path between the specified type and the root, in reverse direction starting from the specified type."""

    if data_type_name not in self.data_type_profiles.keys():
        raise Exception(f"No data type '{data_type_name}' registered.")

    if data_type_name in self._lineages_cache.keys():
        return self._lineages_cache[data_type_name]

    import networkx as nx

    path = nx.shortest_path(
        self.data_type_hierarchy, KIARA_ROOT_TYPE_NAME, data_type_name
    )
    path.remove(KIARA_ROOT_TYPE_NAME)
    self._lineages_cache[data_type_name] = list(reversed(path))
    return self._lineages_cache[data_type_name]

get_type_metadata(self, type_name)

Source code in kiara/registries/types/__init__.py

def get_type_metadata(self, type_name: str) -> DataTypeClassInfo:

    md = self._data_type_metadata.get(type_name, None)
    if md is None:
        md = DataTypeClassInfo.create_from_type_class(
            type_cls=self.get_data_type_cls(type_name=type_name), kiara=self._kiara
        )
        self._data_type_metadata[type_name] = md
    return self._data_type_metadata[type_name]

invalidate_types(self)

Source code in kiara/registries/types/__init__.py

def invalidate_types(self):

    self._data_types = None
    # self._registered_python_classes = None

retrieve_data_type(self, data_type_name, data_type_config=None)

Source code in kiara/registries/types/__init__.py

def retrieve_data_type(
    self, data_type_name: str, data_type_config: Optional[Mapping[str, Any]] = None
) -> DataType:

    if data_type_config is None:
        data_type_config = {}
    else:
        data_type_config = dict(data_type_config)

    if data_type_name not in self.data_type_profiles.keys():
        raise Exception(f"Data type name not registered: {data_type_name}")

    data_type: str = self.data_type_profiles[data_type_name]["type_name"]
    type_config = self.data_type_profiles[data_type_name]["type_config"]

    if data_type_config:
        type_config = dict(type_config)
        type_config.update(data_type_config)

    cls = self.get_data_type_cls(type_name=data_type)

    hash = cls._calculate_data_type_hash(type_config)
    if hash in self._cached_data_type_objects.keys():
        return self._cached_data_type_objects[hash]

    result = cls(**type_config)
    assert result.data_type_hash == hash
    self._cached_data_type_objects[result.data_type_hash] = result
    return result

utils special

CAMEL_TO_SNAKE_REGEX

SUBCLASS_TYPE

WORD_REGEX_PATTERN

logger

string_types

yaml

StringYAML (YAML)

Source code in kiara/utils/__init__.py

class StringYAML(YAML):
    def dump(self, data, stream=None, **kw):
        inefficient = False
        if stream is None:
            inefficient = True
            stream = StringIO()
        YAML.dump(self, data, stream, **kw)
        if inefficient:
            return stream.getvalue()

dump(self, data, stream=None, **kw)

Source code in kiara/utils/__init__.py

def dump(self, data, stream=None, **kw):
    inefficient = False
    if stream is None:
        inefficient = True
        stream = StringIO()
    YAML.dump(self, data, stream, **kw)
    if inefficient:
        return stream.getvalue()

Functions

camel_case_to_snake_case(camel_text, repl='_')

Source code in kiara/utils/__init__.py

def camel_case_to_snake_case(camel_text: str, repl: str = "_"):
    return CAMEL_TO_SNAKE_REGEX.sub(repl, camel_text).lower()

check_valid_field_names(*field_names)

Check whether the provided field names are all valid.

Returns:

Type	Description
List[str]	an iterable of strings with invalid field names

Source code in kiara/utils/__init__.py

def check_valid_field_names(*field_names) -> List[str]:
    """Check whether the provided field names are all valid.

    Returns:
        an iterable of strings with invalid field names
    """

    return [x for x in field_names if x in INVALID_VALUE_NAMES or x.startswith("_")]

create_valid_identifier(text)

Source code in kiara/utils/__init__.py

def create_valid_identifier(text: str):

    return slugify(text, separator="_")

dict_from_cli_args(*args, *, list_keys=None)

Source code in kiara/utils/__init__.py

def dict_from_cli_args(
    *args: str, list_keys: Optional[Iterable[str]] = None
) -> Dict[str, Any]:

    if not args:
        return {}

    config: Dict[str, Any] = {}
    for arg in args:
        if "=" in arg:
            key, value = arg.split("=", maxsplit=1)
            try:
                _v = json.loads(value)
            except Exception:
                _v = value
            part_config = {key: _v}
        elif os.path.isfile(os.path.realpath(os.path.expanduser(arg))):
            path = os.path.realpath(os.path.expanduser(arg))
            part_config = get_data_from_file(path)
            assert isinstance(part_config, Mapping)
        else:
            try:
                part_config = json.loads(arg)
                assert isinstance(part_config, Mapping)
            except Exception:
                raise Exception(f"Could not parse argument into data: {arg}")

        if list_keys is None:
            list_keys = []

        for k, v in part_config.items():
            if k in list_keys:
                config.setdefault(k, []).append(v)
            else:
                if k in config.keys():
                    logger.warning("duplicate.key", old_value=k, new_value=v)
                config[k] = v
    return config

find_free_id(stem, current_ids, sep='_')

Find a free var (or other name) based on a stem string, based on a list of provided existing names.

Parameters:

Name	Type	Description	Default
stem	str	the base string to use	required
current_ids	Iterable[str]	currently existing names	required
method	str	the method to create new names (allowed: 'count' -- for now)	required
method_args	dict	prototing_config for the creation method	required

Returns:

Type	Description
str	a free name

Source code in kiara/utils/__init__.py

def find_free_id(
    stem: str,
    current_ids: Iterable[str],
    sep="_",
) -> str:
    """Find a free var (or other name) based on a stem string, based on a list of provided existing names.

    Args:
        stem (str): the base string to use
        current_ids (Iterable[str]): currently existing names
        method (str): the method to create new names (allowed: 'count' -- for now)
        method_args (dict): prototing_config for the creation method

    Returns:
        str: a free name
    """

    start_count = 1
    if stem not in current_ids:
        return stem

    i = start_count

    # new_name = None
    while True:
        new_name = f"{stem}{sep}{i}"
        if new_name in current_ids:
            i = i + 1
            continue
        break
    return new_name

first_line(text)

Source code in kiara/utils/__init__.py

def first_line(text: str):

    if "\n" in text:
        return text.split("\n")[0].strip()
    else:
        return text

get_auto_workflow_alias(module_type, use_incremental_ids=False)

Return an id for a workflow obj of a provided module class.

If 'use_incremental_ids' is set to True, a unique id is returned.

Parameters:

Name	Type	Description	Default
module_type	str	the name of the module type	required
use_incremental_ids	bool	whether to return a unique (incremental) id	False

Returns:

Type	Description
str	a module id

Source code in kiara/utils/__init__.py

def get_auto_workflow_alias(module_type: str, use_incremental_ids: bool = False) -> str:
    """Return an id for a workflow obj of a provided module class.

    If 'use_incremental_ids' is set to True, a unique id is returned.

    Args:
        module_type (str): the name of the module type
        use_incremental_ids (bool): whether to return a unique (incremental) id

    Returns:
        str: a module id
    """

    if not use_incremental_ids:
        return module_type

    nr = _AUTO_MODULE_ID.setdefault(module_type, 0)
    _AUTO_MODULE_ID[module_type] = nr + 1

    return f"{module_type}_{nr}"

get_data_from_file(path, content_type=None)

Source code in kiara/utils/__init__.py

def get_data_from_file(
    path: Union[str, Path], content_type: Optional[str] = None
) -> Any:

    if isinstance(path, str):
        path = Path(os.path.expanduser(path))

    content = path.read_text()

    if content_type:
        assert content_type in ["json", "yaml"]
    else:
        if path.name.endswith(".json"):
            content_type = "json"
        elif path.name.endswith(".yaml") or path.name.endswith(".yml"):
            content_type = "yaml"
        else:
            raise ValueError(
                "Invalid data format, only 'json' or 'yaml' are supported currently."
            )

    if content_type == "json":
        data = json.loads(content)
    else:
        data = yaml.load(content)

    return data

is_debug()

Source code in kiara/utils/__init__.py

def is_debug() -> bool:

    debug = os.environ.get("DEBUG", "")
    if debug.lower() == "true":
        return True
    else:
        return False

is_develop()

Source code in kiara/utils/__init__.py

def is_develop() -> bool:

    debug = os.environ.get("DEVELOP", "")
    if debug.lower() == "true":
        return True
    else:
        return False

is_rich_renderable(item)

Source code in kiara/utils/__init__.py

def is_rich_renderable(item: Any):
    return isinstance(item, (ConsoleRenderable, RichCast, str))

log_exception(exc)

Source code in kiara/utils/__init__.py

def log_exception(exc: Exception):

    if is_debug():
        traceback.print_exc()

log_message(msg, **data)

Source code in kiara/utils/__init__.py

def log_message(msg: str, **data):

    if is_debug():
        logger.debug(msg, **data)
    # else:
    #     logger.debug(msg, **data)

merge_dicts(*dicts)

Source code in kiara/utils/__init__.py

def merge_dicts(*dicts: Mapping[str, Any]) -> Dict[str, Any]:

    if not dicts:
        return {}

    current: Dict[str, Any] = {}
    for d in dicts:
        dpath.util.merge(current, copy.deepcopy(d))

    return current

orjson_dumps(v, *, default=None, **args)

Source code in kiara/utils/__init__.py

def orjson_dumps(v, *, default=None, **args):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode

    try:
        return orjson.dumps(v, default=default, **args).decode()
    except Exception as e:
        if is_debug():
            print(f"Error dumping json data: {e}")
            from kiara import dbg

            dbg(v)

        raise e

to_camel_case(text)

Source code in kiara/utils/__init__.py

def to_camel_case(text: str) -> str:

    words = WORD_REGEX_PATTERN.split(text)
    return "".join(w.title() for i, w in enumerate(words))

Modules

class_loading

KiaraEntryPointItem

KiaraEntryPointIterable

SUBCLASS_TYPE

logger

Functions

find_all_archive_types()

Find all KiaraArchive subclasses via package entry points.

Source code in kiara/utils/class_loading.py

def find_all_archive_types() -> Dict[str, Type["KiaraArchive"]]:
    """Find all [KiaraArchive][kiara.registries.KiaraArchive] subclasses via package entry points."""

    from kiara.registries import KiaraArchive

    return load_all_subclasses_for_entry_point(
        entry_point_name="kiara.archive_type",
        base_class=KiaraArchive,  # type: ignore
        type_id_key="_archive_type_name",
        type_id_func=_cls_name_id_func,
        attach_python_metadata=False,
    )

find_all_cli_subcommands()

Source code in kiara/utils/class_loading.py

def find_all_cli_subcommands():

    entry_point_name = "kiara.cli_subcommands"
    log2 = logging.getLogger("stevedore")
    out_hdlr = logging.StreamHandler(sys.stdout)
    out_hdlr.setFormatter(
        logging.Formatter(
            f"{entry_point_name} plugin search message/error -> %(message)s"
        )
    )
    out_hdlr.setLevel(logging.INFO)
    log2.addHandler(out_hdlr)
    if is_debug():
        log2.setLevel(logging.DEBUG)
    else:
        out_hdlr.setLevel(logging.INFO)
        log2.setLevel(logging.INFO)

    log_message("events.loading.entry_points", entry_point_name=entry_point_name)

    mgr = ExtensionManager(
        namespace=entry_point_name,
        invoke_on_load=False,
        propagate_map_exceptions=True,
    )

    return [plugin.plugin for plugin in mgr]

find_all_data_types()

Find all [KiaraModule][kiara.module.KiaraModule] subclasses via package entry points.

TODO

Source code in kiara/utils/class_loading.py

def find_all_data_types() -> Dict[str, Type["DataType"]]:
    """Find all [KiaraModule][kiara.module.KiaraModule] subclasses via package entry points.

    TODO
    """

    from kiara.data_types import DataType

    all_data_types = load_all_subclasses_for_entry_point(
        entry_point_name="kiara.data_types",
        base_class=DataType,  # type: ignore
        type_id_key="_data_type_name",
        type_id_func=_cls_name_id_func,
    )

    invalid = [x for x in all_data_types.keys() if "." in x]
    if invalid:
        raise Exception(
            f"Invalid value type name(s), type names can't contain '.': {', '.join(invalid)}"
        )

    return all_data_types

find_all_kiara_model_classes()

Find all [KiaraModule][kiara.module.KiaraModule] subclasses via package entry points.

TODO

Source code in kiara/utils/class_loading.py

def find_all_kiara_model_classes() -> Dict[str, Type["KiaraModel"]]:
    """Find all [KiaraModule][kiara.module.KiaraModule] subclasses via package entry points.

    TODO
    """

    from kiara.models import KiaraModel

    return load_all_subclasses_for_entry_point(
        entry_point_name="kiara.model_classes",
        base_class=KiaraModel,  # type: ignore
        type_id_key="_kiara_model_id",
        type_id_func=_cls_name_id_func,
        attach_python_metadata=False,
    )

find_all_kiara_modules()

Find all [KiaraModule][kiara.module.KiaraModule] subclasses via package entry points.

TODO

Source code in kiara/utils/class_loading.py

def find_all_kiara_modules() -> Dict[str, Type["KiaraModule"]]:
    """Find all [KiaraModule][kiara.module.KiaraModule] subclasses via package entry points.

    TODO
    """

    from kiara.modules import KiaraModule

    modules = load_all_subclasses_for_entry_point(
        entry_point_name="kiara.modules",
        base_class=KiaraModule,  # type: ignore
        type_id_key="_module_type_name",
        attach_python_metadata=True,
    )

    result = {}
    # need to test this, since I couldn't add an abstract method to the KiaraModule class itself (mypy complained because it is potentially overloaded)
    for k, cls in modules.items():

        if not hasattr(cls, "process"):
            # TODO: check signature of process method
            log_message("ignore.module.class", cls=cls, reason="no 'process' method")
            continue

        result[k] = cls
    return result

find_all_kiara_pipeline_paths(skip_errors=False)

Source code in kiara/utils/class_loading.py

def find_all_kiara_pipeline_paths(
    skip_errors: bool = False,
) -> Dict[str, Optional[Mapping[str, Any]]]:

    import logging

    log2 = logging.getLogger("stevedore")
    out_hdlr = logging.StreamHandler(sys.stdout)
    out_hdlr.setFormatter(
        logging.Formatter("kiara pipeline search plugin error -> %(message)s")
    )
    out_hdlr.setLevel(logging.INFO)
    log2.addHandler(out_hdlr)
    log2.setLevel(logging.INFO)

    log_message("events.loading.pipelines")

    mgr = ExtensionManager(
        namespace="kiara.pipelines", invoke_on_load=False, propagate_map_exceptions=True
    )

    paths: Dict[str, Optional[Mapping[str, Any]]] = {}
    # TODO: make sure we load 'core' first?
    for plugin in mgr:

        name = plugin.name
        if (
            isinstance(plugin.plugin, tuple)
            and len(plugin.plugin) >= 1
            and callable(plugin.plugin[0])
        ) or callable(plugin.plugin):
            try:
                if callable(plugin.plugin):
                    func = plugin.plugin
                    args = []
                else:
                    func = plugin.plugin[0]
                    args = plugin.plugin[1:]

                f_args = []
                metadata: Optional[Mapping[str, Any]] = None
                if len(args) >= 1:
                    f_args.append(args[0])
                if len(args) >= 2:
                    metadata = args[1]
                    assert isinstance(metadata, Mapping)
                if len(args) > 3:
                    logger.debug(
                        "ignore.pipeline_lookup_arguments",
                        reason="more than 2 arguments provided",
                        surplus_args=args[2:],
                        path=f_args[0],
                    )

                result = func(f_args[0])
                if not result:
                    continue
                if isinstance(result, str):
                    paths[result] = metadata
                else:
                    for path in paths:
                        assert path not in paths.keys()
                        paths[path] = metadata

            except Exception as e:
                if is_debug():
                    import traceback

                    traceback.print_exc()
                if skip_errors:
                    log_message(
                        "ignore.pipline_entrypoint", entrypoint_name=name, reason=str(e)
                    )
                    continue
                raise Exception(f"Error trying to load plugin '{plugin.plugin}': {e}")
        else:
            if skip_errors:
                log_message(
                    "ignore.pipline_entrypoint",
                    entrypoint_name=name,
                    reason=f"invalid plugin type '{type(plugin.plugin)}'",
                )
                continue
            msg = f"Can't load pipelines for entrypoint '{name}': invalid plugin type '{type(plugin.plugin)}'"
            raise Exception(msg)

    return paths

find_all_operation_types()

Source code in kiara/utils/class_loading.py

def find_all_operation_types() -> Dict[str, Type["OperationType"]]:

    from kiara.operations import OperationType

    result = load_all_subclasses_for_entry_point(
        entry_point_name="kiara.operation_types",
        base_class=OperationType,  # type: ignore
        type_id_key="_operation_type_name",
    )
    return result

find_data_types_under(module)

Source code in kiara/utils/class_loading.py

def find_data_types_under(module: Union[str, ModuleType]) -> List[Type["DataType"]]:

    from kiara.data_types import DataType

    return find_subclasses_under(
        base_class=DataType,  # type: ignore
        python_module=module,
    )

find_kiara_model_classes_under(module)

Source code in kiara/utils/class_loading.py

def find_kiara_model_classes_under(
    module: Union[str, ModuleType]
) -> List[Type["KiaraModel"]]:

    from kiara.models import KiaraModel

    result = find_subclasses_under(
        base_class=KiaraModel,  # type: ignore
        python_module=module,
    )

    return result

find_kiara_modules_under(module)

Source code in kiara/utils/class_loading.py

def find_kiara_modules_under(
    module: Union[str, ModuleType],
) -> List[Type["KiaraModule"]]:

    from kiara.modules import KiaraModule

    return find_subclasses_under(
        base_class=KiaraModule,  # type: ignore
        python_module=module,
    )

find_operations_under(module)

Source code in kiara/utils/class_loading.py

def find_operations_under(
    module: Union[str, ModuleType]
) -> List[Type["OperationType"]]:

    from kiara.operations import OperationType

    return find_subclasses_under(
        base_class=OperationType,  # type: ignore
        python_module=module,
    )

find_pipeline_base_path_for_module(module)

Source code in kiara/utils/class_loading.py

def find_pipeline_base_path_for_module(module: Union[str, ModuleType]) -> Optional[str]:

    if hasattr(sys, "frozen"):
        raise NotImplementedError("Pyinstaller bundling not supported yet.")

    if isinstance(module, str):
        module = importlib.import_module(module)

    module_file = module.__file__
    assert module_file is not None
    path = os.path.dirname(module_file)

    if not os.path.exists:
        log_message("ignore.pipeline_folder", path=path, reason="folder does not exist")
        return None

    return path

find_subclasses_under(base_class, python_module)

Find all (non-abstract) subclasses of a base class that live under a module (recursively).

Parameters:

Name	Type	Description	Default
base_class	Type[~SUBCLASS_TYPE]	the parent class	required
python_module	Union[str, module]	the Python module to search	required

Returns:

Type	Description
List[Type[~SUBCLASS_TYPE]]	a list of all subclasses

Source code in kiara/utils/class_loading.py

def find_subclasses_under(
    base_class: Type[SUBCLASS_TYPE],
    python_module: Union[str, ModuleType],
) -> List[Type[SUBCLASS_TYPE]]:
    """Find all (non-abstract) subclasses of a base class that live under a module (recursively).

    Arguments:
        base_class: the parent class
        python_module: the Python module to search

    Returns:
        a list of all subclasses
    """

    if hasattr(sys, "frozen"):
        raise NotImplementedError("Pyinstaller bundling not supported yet.")

    try:
        if isinstance(python_module, str):
            python_module = importlib.import_module(python_module)

        _import_modules_recursively(python_module)
    except Exception as e:
        log_exception(e)
        log_message("ignore.python_module", module=str(python_module), reason=str(e))
        return []

    subclasses: Iterable[Type[SUBCLASS_TYPE]] = _get_all_subclasses(base_class)

    result = []
    for sc in subclasses:

        if not sc.__module__.startswith(python_module.__name__):
            continue

        result.append(sc)

    return result

load_all_subclasses_for_entry_point(entry_point_name, base_class, ignore_abstract_classes=True, type_id_key=None, type_id_func=None, type_id_no_attach=False, attach_python_metadata=False)

Find all subclasses of a base class via package entry points.

Parameters:

Name	Type	Description	Default
entry_point_name	str	the entry point name to query entries for	required
base_class	Type[~SUBCLASS_TYPE]	the base class to look for	required
ignore_abstract_classes	bool	whether to include abstract classes in the result	True
type_id_key	Optional[str]	if provided, the found classes will have their id attached as an attribute, using the value of this as the name. if an attribute of this name already exists, it will be used as id without further processing	None
type_id_func	Callable	a function to take the found class as input, and returns a string representing the id of the class. By default, the module path + "." + class name (snake-case) is used (minus the string 'kiara_modules.'', if it exists at the beginning	None
type_id_no_attach	bool	in case you want to use the type_id_key to set the id, but don't want it attached to classes that don't have it, set this to true. In most cases, you won't need this option	False
attach_python_metadata	Union[bool, str]	whether to attach a PythonClass metadata model to the class. By default, '_python_class' is used as attribute name if this argument is 'True', If this argument is a string, that will be used as name instead.	False

Source code in kiara/utils/class_loading.py

def load_all_subclasses_for_entry_point(
    entry_point_name: str,
    base_class: Type[SUBCLASS_TYPE],
    ignore_abstract_classes: bool = True,
    type_id_key: Optional[str] = None,
    type_id_func: Callable = None,
    type_id_no_attach: bool = False,
    attach_python_metadata: Union[bool, str] = False,
) -> Dict[str, Type[SUBCLASS_TYPE]]:
    """Find all subclasses of a base class via package entry points.

    Arguments:
        entry_point_name: the entry point name to query entries for
        base_class: the base class to look for
        ignore_abstract_classes: whether to include abstract classes in the result
        type_id_key: if provided, the found classes will have their id attached as an attribute, using the value of this as the name. if an attribute of this name already exists, it will be used as id without further processing
        type_id_func: a function to take the found class as input, and returns a string representing the id of the class. By default, the module path + "." + class name (snake-case) is used (minus the string 'kiara_modules.<project_name>'', if it exists at the beginning
        type_id_no_attach: in case you want to use the type_id_key to set the id, but don't want it attached to classes that don't have it, set this to true. In most cases, you won't need this option
        attach_python_metadata: whether to attach a [PythonClass][kiara.models.python_class.PythonClass] metadata model to the class. By default, '_python_class' is used as attribute name if this argument is 'True', If this argument is a string, that will be used as name instead.
    """

    log2 = logging.getLogger("stevedore")
    out_hdlr = logging.StreamHandler(sys.stdout)
    out_hdlr.setFormatter(
        logging.Formatter(
            f"{entry_point_name} plugin search message/error -> %(message)s"
        )
    )
    out_hdlr.setLevel(logging.INFO)
    log2.addHandler(out_hdlr)
    if is_debug():
        log2.setLevel(logging.DEBUG)
    else:
        out_hdlr.setLevel(logging.INFO)
        log2.setLevel(logging.INFO)

    log_message("events.loading.entry_points", entry_point_name=entry_point_name)

    mgr = ExtensionManager(
        namespace=entry_point_name,
        invoke_on_load=False,
        propagate_map_exceptions=True,
    )

    result_entrypoints: Dict[str, Type[SUBCLASS_TYPE]] = {}
    result_dynamic: Dict[str, Type[SUBCLASS_TYPE]] = {}

    for plugin in mgr:
        name = plugin.name

        if isinstance(plugin.plugin, type):
            # this means an actual (sub-)class was provided in the entrypoint

            cls = plugin.plugin
            if not issubclass(cls, base_class):
                log_message(
                    "ignore.entrypoint",
                    entry_point=name,
                    base_class=base_class,
                    sub_class=plugin.plugin,
                    reason=f"Entry point reference not a subclass of '{base_class}'.",
                )
                continue

            _process_subclass(
                sub_class=cls,
                base_class=base_class,
                type_id_key=type_id_key,
                type_id_func=type_id_func,
                type_id_no_attach=type_id_no_attach,
                attach_python_metadata=attach_python_metadata,
                ignore_abstract_classes=ignore_abstract_classes,
            )

            result_entrypoints[name] = cls
        elif (
            isinstance(plugin.plugin, tuple)
            and len(plugin.plugin) >= 1
            and callable(plugin.plugin[0])
        ) or callable(plugin.plugin):
            try:
                if callable(plugin.plugin):
                    func = plugin.plugin
                    args = []
                else:
                    func = plugin.plugin[0]
                    args = plugin.plugin[1:]
                classes = func(*args)
            except Exception as e:
                if is_debug():
                    import traceback

                    traceback.print_exc()
                raise Exception(f"Error trying to load plugin '{plugin.plugin}': {e}")

            for sub_class in classes:
                type_id = _process_subclass(
                    sub_class=sub_class,
                    base_class=base_class,
                    type_id_key=type_id_key,
                    type_id_func=type_id_func,
                    type_id_no_attach=type_id_no_attach,
                    attach_python_metadata=attach_python_metadata,
                    ignore_abstract_classes=ignore_abstract_classes,
                )

                if type_id is None:
                    continue

                if type_id in result_dynamic.keys():
                    raise Exception(
                        f"Duplicate type id '{type_id}' for type {entry_point_name}: {result_dynamic[type_id]} -- {sub_class}"
                    )
                result_dynamic[type_id] = sub_class

        else:
            raise Exception(
                f"Can't load subclasses for entry point {entry_point_name} and base class {base_class}: invalid plugin type {type(plugin.plugin)}"
            )

    for k, v in result_dynamic.items():
        if k in result_entrypoints.keys():
            msg = f"Duplicate item name '{k}' for type {entry_point_name}: {v} -- {result_entrypoints[k]}."
            try:
                if type_id_key not in v.__dict__.keys():
                    msg = f"{msg} Most likely the name is picked up from a subclass, try to add a '{type_id_key}' class attribute to your implementing class, with the name you want to give your type as value."
            except Exception:
                pass

            raise Exception(msg)
        result_entrypoints[k] = v

    return result_entrypoints

cli

F

FC

Classes

OutputFormat (Enum)

An enumeration.

Source code in kiara/utils/cli.py

class OutputFormat(Enum):
    @classmethod
    def as_dict(cls):
        return {i.name: i.value for i in cls}

    @classmethod
    def keys_as_list(cls):
        return cls._member_names_

    @classmethod
    def values_as_list(cls):
        return [i.value for i in cls]

    TERMINAL = "terminal"
    HTML = "html"
    JSON = "json"
    JSON_INCL_SCHEMA = "json-incl-schema"
    JSON_SCHEMA = "json-schema"

HTML

JSON

JSON_INCL_SCHEMA

JSON_SCHEMA

TERMINAL

Functions

output_format_option(*param_decls)

Attaches an option to the command. All positional arguments are passed as parameter declarations to :class:Option; all keyword arguments are forwarded unchanged (except cls). This is equivalent to creating an :class:Option instance manually and attaching it to the :attr:Command.params list.

:param cls: the option class to instantiate. This defaults to :class:Option.

Source code in kiara/utils/cli.py

def output_format_option(*param_decls: str) -> Callable[[FC], FC]:
    """Attaches an option to the command.  All positional arguments are
    passed as parameter declarations to :class:`Option`; all keyword
    arguments are forwarded unchanged (except ``cls``).
    This is equivalent to creating an :class:`Option` instance manually
    and attaching it to the :attr:`Command.params` list.

    :param cls: the option class to instantiate.  This defaults to
                :class:`Option`.
    """

    if not param_decls:
        param_decls = ("--format", "-f")

    attrs = {
        "help": "The output format. Defaults to 'terminal'.",
        "type": click.Choice(OutputFormat.values_as_list()),
    }

    def decorator(f: FC) -> FC:
        # Issue 926, copy attrs, so pre-defined options can re-use the same cls=
        option_attrs = attrs.copy()
        OptionClass = option_attrs.pop("cls", None) or Option
        _param_memo(f, OptionClass(param_decls, **option_attrs))  # type: ignore
        return f

    return decorator

render_json_schema_str(model)

Source code in kiara/utils/cli.py

def render_json_schema_str(model: BaseModel):

    try:
        json_str = model.schema_json(option=orjson.OPT_INDENT_2)
    except TypeError:
        json_str = model.schema_json(indent=2)

    return json_str

render_json_str(model)

Source code in kiara/utils/cli.py

def render_json_str(model: BaseModel):

    try:
        json_str = model.json(option=orjson.OPT_INDENT_2 | orjson.OPT_NON_STR_KEYS)
    except TypeError:
        json_str = model.json(indent=2)

    return json_str

terminal_print(msg=None, in_panel=None, rich_config=None, empty_line_before=False, **config)

Source code in kiara/utils/cli.py

def terminal_print(
    msg: Any = None,
    in_panel: Optional[str] = None,
    rich_config: Optional[Mapping[str, Any]] = None,
    empty_line_before: bool = False,
    **config: Any,
) -> None:

    if msg is None:
        msg = ""
    console = get_console()

    msg = extract_renderable(msg, render_config=config)
    # if hasattr(msg, "create_renderable"):
    #     msg = msg.create_renderable(**config)  # type: ignore

    if in_panel is not None:
        msg = Panel(msg, title_align="left", title=in_panel)

    if empty_line_before:
        console.print()
    if rich_config:
        console.print(msg, **rich_config)
    else:
        console.print(msg)

terminal_print_model(*models, *, format=None, empty_line_before=None, in_panel=None, **render_config)

Source code in kiara/utils/cli.py

def terminal_print_model(
    *models: BaseModel,
    format: Union[None, OutputFormat, str] = None,
    empty_line_before: Optional[bool] = None,
    in_panel: Optional[str] = None,
    **render_config: Any,
):

    if format is None:
        format = OutputFormat.TERMINAL

    if isinstance(format, str):
        format = OutputFormat(format)

    if empty_line_before is None:
        if format == OutputFormat.TERMINAL:
            empty_line_before = True
        else:
            empty_line_before = False

    if format == OutputFormat.TERMINAL:
        if len(models) == 1:
            terminal_print(
                models[0],
                in_panel=in_panel,
                empty_line_before=empty_line_before,
                **render_config,
            )
        else:
            rg = []
            for model in models[0:-1]:
                renderable = extract_renderable(model, render_config)
                rg.append(renderable)
                rg.append(Rule(style="b"))
            last = extract_renderable(models[-1], render_config)
            rg.append(last)
            group = Group(*rg)
            terminal_print(group, in_panel=in_panel, **render_config)
    elif format == OutputFormat.JSON:
        if len(models) == 1:
            json_str = render_json_str(models[0])
            syntax = Syntax(json_str, "json", background_color="default")
            terminal_print(
                syntax,
                empty_line_before=empty_line_before,
                rich_config={"soft_wrap": True},
            )
        else:
            json_strs = []
            for model in models:
                json_str = render_json_str(model)
                json_strs.append(json_str)

            json_str_full = "[" + ",\n".join(json_strs) + "]"
            syntax = Syntax(json_str_full, "json", background_color="default")
            terminal_print(
                syntax,
                empty_line_before=empty_line_before,
                rich_config={"soft_wrap": True},
            )

    elif format == OutputFormat.JSON_SCHEMA:
        if len(models) == 1:
            syntax = Syntax(
                models[0].schema_json(option=orjson.OPT_INDENT_2),
                "json",
                background_color="default",
            )
            terminal_print(
                syntax,
                empty_line_before=empty_line_before,
                rich_config={"soft_wrap": True},
            )
        else:
            json_strs = []
            for model in models:
                json_strs.append(render_json_schema_str(model))
            json_str_full = "[" + ",\n".join(json_strs) + "]"
            syntax = Syntax(json_str_full, "json", background_color="default")
            terminal_print(
                syntax,
                empty_line_before=empty_line_before,
                rich_config={"soft_wrap": True},
            )
    elif format == OutputFormat.JSON_INCL_SCHEMA:
        if len(models) == 1:
            data = models[0].dict()
            schema = models[0].schema()
            all = {"data": data, "schema": schema}
            json_str = orjson_dumps(all, option=orjson.OPT_INDENT_2)
            syntax = Syntax(json_str, "json", background_color="default")
            terminal_print(
                syntax,
                empty_line_before=empty_line_before,
                rich_config={"soft_wrap": True},
            )
        else:
            all_data = []
            for model in models:
                data = model.dict()
                schema = model.schema()
                all_data.append({"data": data, "schema": schema})
            json_str = orjson_dumps(all_data, option=orjson.OPT_INDENT_2)
            # print(json_str)
            syntax = Syntax(json_str, "json", background_color="default")
            terminal_print(
                syntax,
                empty_line_before=empty_line_before,
                rich_config={"soft_wrap": True},
            )

    elif format == OutputFormat.HTML:

        all_html = ""
        for model in models:
            if hasattr(model, "create_html"):
                html = model.create_html()  # type: ignore
                all_html = f"{all_html}\n{html}"
            else:
                raise NotImplementedError()

        syntax = Syntax(all_html, "html", background_color="default")
        terminal_print(
            syntax, empty_line_before=empty_line_before, rich_config={"soft_wrap": True}
        )

concurrency

Classes

ThreadSaveCounter

A thread-safe counter, can be used in kiara modules to update completion percentage.

Source code in kiara/utils/concurrency.py

class ThreadSaveCounter(object):
    """A thread-safe counter, can be used in kiara modules to update completion percentage."""

    def __init__(self):

        self._current = 0
        self._lock = threading.Lock()

    @property
    def current(self):
        return self._current

    def current_percent(self, total: int) -> int:

        return int((self.current / total) * 100)

    def increment(self):

        with self._lock:
            self._current += 1
            return self._current

    def decrement(self):

        with self._lock:
            self._current -= 1
            return self._current

current property readonly

current_percent(self, total)

Source code in kiara/utils/concurrency.py

def current_percent(self, total: int) -> int:

    return int((self.current / total) * 100)

decrement(self)

Source code in kiara/utils/concurrency.py

def decrement(self):

    with self._lock:
        self._current -= 1
        return self._current

increment(self)

Source code in kiara/utils/concurrency.py

def increment(self):

    with self._lock:
        self._current += 1
        return self._current

data

logger

pretty_print_data(kiara, value_id, target_type='terminal_renderable', **render_config)

Source code in kiara/utils/data.py

def pretty_print_data(
    kiara: "Kiara",
    value_id: uuid.UUID,
    target_type="terminal_renderable",
    **render_config: Any,
) -> Any:

    value = kiara.data_registry.get_value(value_id=value_id)

    op_type: PrettyPrintOperationType = kiara.operation_registry.get_operation_type("pretty_print")  # type: ignore

    try:
        op: Optional[Operation] = op_type.get_operation_for_render_combination(
            source_type=value.value_schema.type, target_type=target_type
        )
    except Exception as e:

        logger.debug(
            "error.pretty_print",
            source_type=value.value_schema.type,
            target_type=target_type,
            error=e,
        )

        op = None
        if target_type == "terminal_renderable":
            try:
                op = op_type.get_operation_for_render_combination(
                    source_type="any", target_type="string"
                )
            except Exception:
                pass

    if op is None:
        raise Exception(
            f"Can't find operation to render '{value.value_schema.type}' as '{target_type}."
        )

    result = op.run(kiara=kiara, inputs={"value": value})
    rendered = result.get_value_data("rendered_value")
    return rendered

render_value(kiara, value_id, target_type='terminal_renderable', render_instruction=None)

Source code in kiara/utils/data.py

def render_value(
    kiara: "Kiara",
    value_id: uuid.UUID,
    target_type="terminal_renderable",
    render_instruction: Optional[RenderInstruction] = None,
) -> RenderValueResult:

    value = kiara.data_registry.get_value(value_id=value_id)
    op_type: RenderValueOperationType = kiara.operation_registry.get_operation_type("render_value")  # type: ignore

    ops = op_type.get_render_operations_for_source_type(value.data_type_name)
    if target_type not in ops.keys():
        if not ops:
            msg = f"No render operations registered for source type '{value.data_type_name}'."
        else:
            msg = f"Registered target types for source type '{value.data}': {', '.join(ops.keys())}."
        raise Exception(
            f"No render operation for source type '{value.data_type_name}' to target type '{target_type}' registered. {msg}"
        )

    op = ops[target_type]
    result = op.run(
        kiara=kiara, inputs={"value": value, "render_instruction": render_instruction}
    )

    return RenderValueResult(
        rendered=result.get_value_data("rendered_value"),
        metadata=result.get_value_data("render_metadata"),
    )

db

get_kiara_db_url(base_path)

Source code in kiara/utils/db.py

def get_kiara_db_url(base_path: str):

    abs_path = os.path.abspath(os.path.expanduser(base_path))
    db_url = f"sqlite+pysqlite:///{abs_path}/kiara.db"
    return db_url

orm_json_deserialize(obj)

Source code in kiara/utils/db.py

def orm_json_deserialize(obj: str) -> Any:
    return orjson.loads(obj)

orm_json_serialize(obj)

Source code in kiara/utils/db.py

def orm_json_serialize(obj: Any) -> str:

    if hasattr(obj, "json"):
        return obj.json()

    if isinstance(obj, str):
        return obj
    elif isinstance(obj, Mapping):
        return orjson_dumps(obj, default=None)
    else:
        raise Exception(f"Unsupported type for json serialization: {type(obj)}")

doc

extract_doc_from_cls(cls, only_first_line=False)

Source code in kiara/utils/doc.py

def extract_doc_from_cls(cls: typing.Type, only_first_line: bool = False):

    doc = cls.__doc__
    if not doc:
        doc = DEFAULT_NO_DESC_VALUE
    else:
        doc = cleandoc(doc)

    if only_first_line:
        return first_line(doc)
    else:
        return doc.strip()

global_metadata

get_metadata_for_python_module_or_class(module_or_class)

Source code in kiara/utils/global_metadata.py

@lru_cache()
def get_metadata_for_python_module_or_class(
    module_or_class: typing.Union[ModuleType, typing.Type]
) -> typing.List[typing.Dict[str, typing.Any]]:

    metadata: typing.List[typing.Dict[str, typing.Any]] = []

    if isinstance(module_or_class, type):
        if hasattr(module_or_class, KIARA_MODULE_METADATA_ATTRIBUTE):
            md = getattr(module_or_class, KIARA_MODULE_METADATA_ATTRIBUTE)
            assert isinstance(md, typing.Mapping)
            metadata.append(md)  # type: ignore
        _module_or_class: typing.Union[
            str, ModuleType, typing.Type
        ] = module_or_class.__module__
    else:
        _module_or_class = module_or_class

    current_module = _module_or_class
    while current_module:

        if isinstance(current_module, str):
            current_module = importlib.import_module(current_module)

        if hasattr(current_module, KIARA_MODULE_METADATA_ATTRIBUTE):
            md = getattr(current_module, KIARA_MODULE_METADATA_ATTRIBUTE)
            assert isinstance(md, typing.Mapping)
            metadata.append(md)  # type: ignore

        if "." in current_module.__name__:
            current_module = ".".join(current_module.__name__.split(".")[0:-1])
        else:
            current_module = ""

    metadata.reverse()
    return metadata

graphs

print_ascii_graph(graph)

Source code in kiara/utils/graphs.py

def print_ascii_graph(graph: nx.Graph):

    try:
        from asciinet import graph_to_ascii
    except:  # noqa
        print(
            "\nCan't print graph on terminal, package 'asciinet' not available. Please install it into the current virtualenv using:\n\npip install 'git+https://github.com/cosminbasca/asciinet.git#egg=asciinet&subdirectory=pyasciinet'"
        )
        return

    try:
        from asciinet._libutil import check_java

        check_java("Java ")
    except Exception as e:
        print(e)
        print(
            "\nJava is currently necessary to print ascii graph. This might change in the future, but to use this functionality please install a JRE."
        )
        return

    print(graph_to_ascii(graph))

hashfs special

HashFS is a content-addressable file management system. What does that mean? Simply, that HashFS manages a directory where files are saved based on the file's hash.

Typical use cases for this kind of system are ones where:

• Files are written once and never change (e.g. image storage).
• It's desirable to have no duplicate files (e.g. user uploads).
• File metadata is stored elsewhere (e.g. in a database).

Adapted from: https://github.com/dgilland/hashfs

License

The MIT License (MIT)

Copyright (c) 2015, Derrick Gilland

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Classes

HashAddress (HashAddress)

File address containing file's path on disk and it's content hash ID.

Attributes:

Name	Type	Description
id	str	Hash ID (hexdigest) of file contents.
relpath	str	Relative path location to :attr:HashFS.root.
abspath	str	Absoluate path location of file on disk.
is_duplicate	boolean	Whether the hash address created was a duplicate of a previously existing file. Can only be True after a put operation. Defaults to False.

Source code in kiara/utils/hashfs/__init__.py

class HashAddress(
    namedtuple("HashAddress", ["id", "relpath", "abspath", "is_duplicate"])
):
    """File address containing file's path on disk and it's content hash ID.

    Attributes:
        id (str): Hash ID (hexdigest) of file contents.
        relpath (str): Relative path location to :attr:`HashFS.root`.
        abspath (str): Absoluate path location of file on disk.
        is_duplicate (boolean, optional): Whether the hash address created was
            a duplicate of a previously existing file. Can only be ``True``
            after a put operation. Defaults to ``False``.
    """

    def __new__(cls, id, relpath, abspath, is_duplicate=False):
        return super(HashAddress, cls).__new__(cls, id, relpath, abspath, is_duplicate)  # type: ignore

HashFS

Content addressable file manager.

Attributes:

Name	Type	Description
root	str	Directory path used as root of storage space.
depth	int	Depth of subfolders to create when saving a file.
width	int	Width of each subfolder to create when saving a file.
algorithm	str	Hash algorithm to use when computing file hash. Algorithm should be available in hashlib module. Defaults to 'sha256'.
fmode	int	File mode permission to set when adding files to directory. Defaults to 0o664 which allows owner/group to read/write and everyone else to read.
dmode	int	Directory mode permission to set for subdirectories. Defaults to 0o755 which allows owner/group to read/write and everyone else to read and everyone to execute.

Source code in kiara/utils/hashfs/__init__.py

class HashFS(object):
    """Content addressable file manager.

    Attributes:
        root (str): Directory path used as root of storage space.
        depth (int, optional): Depth of subfolders to create when saving a
            file.
        width (int, optional): Width of each subfolder to create when saving a
            file.
        algorithm (str): Hash algorithm to use when computing file hash.
            Algorithm should be available in ``hashlib`` module. Defaults to
            ``'sha256'``.
        fmode (int, optional): File mode permission to set when adding files to
            directory. Defaults to ``0o664`` which allows owner/group to
            read/write and everyone else to read.
        dmode (int, optional): Directory mode permission to set for
            subdirectories. Defaults to ``0o755`` which allows owner/group to
            read/write and everyone else to read and everyone to execute.
    """

    def __init__(
        self,
        root: str,
        depth: int = 4,
        width: int = 1,
        algorithm: str = "sha256",
        fmode=0o664,
        dmode=0o755,
    ):
        self.root: str = os.path.realpath(root)
        self.depth: int = depth
        self.width: int = width
        self.algorithm: str = algorithm
        self.fmode = fmode
        self.dmode = dmode

    def put(self, file: BinaryIO) -> "HashAddress":
        """Store contents of `file` on disk using its content hash for the
        address.

        Args:
            file (mixed): Readable object or path to file.

        Returns:
            HashAddress: File's hash address.
        """
        stream = Stream(file)

        with closing(stream):
            id = self.computehash(stream)
            filepath, is_duplicate = self._copy(stream, id)

        return HashAddress(id, self.relpath(filepath), filepath, is_duplicate)

    def put_with_precomputed_hash(
        self, file: Union[str, Path, BinaryIO], hash_id: str
    ) -> "HashAddress":

        stream = Stream(file)
        with closing(stream):
            filepath, is_duplicate = self._copy(stream=stream, id=hash_id)

        return HashAddress(hash_id, self.relpath(filepath), filepath, is_duplicate)

    def _copy(self, stream: "Stream", id: str):
        """Copy the contents of `stream` onto disk with an optional file
        extension appended. The copy process uses a temporary file to store the
        initial contents and then moves that file to it's final location.
        """

        filepath = self.idpath(id)

        if not os.path.isfile(filepath):
            # Only move file if it doesn't already exist.
            is_duplicate = False
            fname = self._mktempfile(stream)
            self.makepath(os.path.dirname(filepath))
            shutil.move(fname, filepath)
        else:
            is_duplicate = True

        return (filepath, is_duplicate)

    def _mktempfile(self, stream):
        """Create a named temporary file from a :class:`Stream` object and
        return its filename.
        """
        tmp = NamedTemporaryFile(delete=False)

        if self.fmode is not None:
            oldmask = os.umask(0)

            try:
                os.chmod(tmp.name, self.fmode)
            finally:
                os.umask(oldmask)

        for data in stream:
            tmp.write(to_bytes(data))

        tmp.close()

        return tmp.name

    def get(self, file):
        """Return :class:`HashAdress` from given id or path. If `file` does not
        refer to a valid file, then ``None`` is returned.

        Args:
            file (str): Address ID or path of file.

        Returns:
            HashAddress: File's hash address.
        """
        realpath = self.realpath(file)

        if realpath is None:
            return None
        else:
            return HashAddress(self.unshard(realpath), self.relpath(realpath), realpath)

    def open(self, file, mode="rb"):
        """Return open buffer object from given id or path.

        Args:
            file (str): Address ID or path of file.
            mode (str, optional): Mode to open file in. Defaults to ``'rb'``.

        Returns:
            Buffer: An ``io`` buffer dependent on the `mode`.

        Raises:
            IOError: If file doesn't exist.
        """
        realpath = self.realpath(file)
        if realpath is None:
            raise IOError("Could not locate file: {0}".format(file))

        return io.open(realpath, mode)

    def delete(self, file):
        """Delete file using id or path. Remove any empty directories after
        deleting. No exception is raised if file doesn't exist.

        Args:
            file (str): Address ID or path of file.
        """
        realpath = self.realpath(file)
        if realpath is None:
            return

        try:
            os.remove(realpath)
        except OSError:  # pragma: no cover
            pass
        else:
            self.remove_empty(os.path.dirname(realpath))

    def remove_empty(self, subpath):
        """Successively remove all empty folders starting with `subpath` and
        proceeding "up" through directory tree until reaching the :attr:`root`
        folder.
        """
        # Don't attempt to remove any folders if subpath is not a
        # subdirectory of the root directory.
        if not self.haspath(subpath):
            return

        while subpath != self.root:
            if len(os.listdir(subpath)) > 0 or os.path.islink(subpath):
                break
            os.rmdir(subpath)
            subpath = os.path.dirname(subpath)

    def files(self):
        """Return generator that yields all files in the :attr:`root`
        directory.
        """
        for folder, subfolders, files in walk(self.root):
            for file in files:
                yield os.path.abspath(os.path.join(folder, file))

    def folders(self):
        """Return generator that yields all folders in the :attr:`root`
        directory that contain files.
        """
        for folder, subfolders, files in walk(self.root):
            if files:
                yield folder

    def count(self):
        """Return count of the number of files in the :attr:`root` directory."""
        count = 0
        for _ in self:
            count += 1
        return count

    def size(self):
        """Return the total size in bytes of all files in the :attr:`root`
        directory.
        """
        total = 0

        for path in self.files():
            total += os.path.getsize(path)

        return total

    def exists(self, file):
        """Check whether a given file id or path exists on disk."""
        return bool(self.realpath(file))

    def haspath(self, path):
        """Return whether `path` is a subdirectory of the :attr:`root`
        directory.
        """
        return issubdir(path, self.root)

    def makepath(self, path):
        """Physically create the folder path on disk."""
        try:
            os.makedirs(path, self.dmode)
        except FileExistsError:
            assert os.path.isdir(path), "expected {} to be a directory".format(path)

    def relpath(self, path):
        """Return `path` relative to the :attr:`root` directory."""
        return os.path.relpath(path, self.root)

    def realpath(self, file):
        """Attempt to determine the real path of a file id or path through
        successive checking of candidate paths. If the real path is stored with
        an extension, the path is considered a match if the basename matches
        the expected file path of the id.
        """
        # Check for absoluate path.
        if os.path.isfile(file):
            return file

        # Check for relative path.
        relpath = os.path.join(self.root, file)
        if os.path.isfile(relpath):
            return relpath

        # Check for sharded path.
        filepath = self.idpath(file)
        if os.path.isfile(filepath):
            return filepath

        # Check for sharded path with any extension.
        paths = glob.glob("{0}.*".format(filepath))
        if paths:
            return paths[0]

        # Could not determine a match.
        return None

    def idpath(self, id):
        """Build the file path for a given hash id. Optionally, append a
        file extension.
        """
        paths = self.shard(id)

        return os.path.join(self.root, *paths)

    def computehash(self, stream) -> str:
        """Compute hash of file using :attr:`algorithm`."""
        hashobj = hashlib.new(self.algorithm)
        for data in stream:
            hashobj.update(to_bytes(data))
        return hashobj.hexdigest()

    def shard(self, id):
        """Shard content ID into subfolders."""
        return shard(id, self.depth, self.width)

    def unshard(self, path):
        """Unshard path to determine hash value."""
        if not self.haspath(path):
            raise ValueError(
                "Cannot unshard path. The path {0!r} is not "
                "a subdirectory of the root directory {1!r}".format(path, self.root)
            )

        return os.path.splitext(self.relpath(path))[0].replace(os.sep, "")

    def repair(self):
        """Repair any file locations whose content address doesn't match it's
        file path.
        """
        repaired = []
        corrupted = tuple(self.corrupted())
        oldmask = os.umask(0)

        try:
            for path, address in corrupted:
                if os.path.isfile(address.abspath):
                    # File already exists so just delete corrupted path.
                    os.remove(path)
                else:
                    # File doesn't exists so move it.
                    self.makepath(os.path.dirname(address.abspath))
                    shutil.move(path, address.abspath)

                os.chmod(address.abspath, self.fmode)
                repaired.append((path, address))
        finally:
            os.umask(oldmask)

        return repaired

    def corrupted(self):
        """Return generator that yields corrupted files as ``(path, address)``
        where ``path`` is the path of the corrupted file and ``address`` is
        the :class:`HashAddress` of the expected location.
        """
        for path in self.files():
            stream = Stream(path)

            with closing(stream):
                id = self.computehash(stream)

            expected_path = self.idpath(id)

            if expected_path != path:
                yield (
                    path,
                    HashAddress(id, self.relpath(expected_path), expected_path),
                )

    def __contains__(self, file):
        """Return whether a given file id or path is contained in the
        :attr:`root` directory.
        """
        return self.exists(file)

    def __iter__(self):
        """Iterate over all files in the :attr:`root` directory."""
        return self.files()

    def __len__(self):
        """Return count of the number of files in the :attr:`root` directory."""
        return self.count()

Methods

computehash(self, stream)

Compute hash of file using :attr:algorithm.

Source code in kiara/utils/hashfs/__init__.py

def computehash(self, stream) -> str:
    """Compute hash of file using :attr:`algorithm`."""
    hashobj = hashlib.new(self.algorithm)
    for data in stream:
        hashobj.update(to_bytes(data))
    return hashobj.hexdigest()

corrupted(self)

Return generator that yields corrupted files as (path, address) where path is the path of the corrupted file and address is the :class:HashAddress of the expected location.

Source code in kiara/utils/hashfs/__init__.py

def corrupted(self):
    """Return generator that yields corrupted files as ``(path, address)``
    where ``path`` is the path of the corrupted file and ``address`` is
    the :class:`HashAddress` of the expected location.
    """
    for path in self.files():
        stream = Stream(path)

        with closing(stream):
            id = self.computehash(stream)

        expected_path = self.idpath(id)

        if expected_path != path:
            yield (
                path,
                HashAddress(id, self.relpath(expected_path), expected_path),
            )

count(self)

Return count of the number of files in the :attr:root directory.

Source code in kiara/utils/hashfs/__init__.py

def count(self):
    """Return count of the number of files in the :attr:`root` directory."""
    count = 0
    for _ in self:
        count += 1
    return count

delete(self, file)

Delete file using id or path. Remove any empty directories after deleting. No exception is raised if file doesn't exist.

Parameters:

Name	Type	Description	Default
file	str	Address ID or path of file.	required

Source code in kiara/utils/hashfs/__init__.py

def delete(self, file):
    """Delete file using id or path. Remove any empty directories after
    deleting. No exception is raised if file doesn't exist.

    Args:
        file (str): Address ID or path of file.
    """
    realpath = self.realpath(file)
    if realpath is None:
        return

    try:
        os.remove(realpath)
    except OSError:  # pragma: no cover
        pass
    else:
        self.remove_empty(os.path.dirname(realpath))

exists(self, file)

Check whether a given file id or path exists on disk.

Source code in kiara/utils/hashfs/__init__.py

def exists(self, file):
    """Check whether a given file id or path exists on disk."""
    return bool(self.realpath(file))

files(self)

Return generator that yields all files in the :attr:root directory.

Source code in kiara/utils/hashfs/__init__.py

def files(self):
    """Return generator that yields all files in the :attr:`root`
    directory.
    """
    for folder, subfolders, files in walk(self.root):
        for file in files:
            yield os.path.abspath(os.path.join(folder, file))

folders(self)

Return generator that yields all folders in the :attr:root directory that contain files.

Source code in kiara/utils/hashfs/__init__.py

def folders(self):
    """Return generator that yields all folders in the :attr:`root`
    directory that contain files.
    """
    for folder, subfolders, files in walk(self.root):
        if files:
            yield folder

get(self, file)

Return :class:HashAdress from given id or path. If file does not refer to a valid file, then None is returned.

Parameters:

Name	Type	Description	Default
file	str	Address ID or path of file.	required

Returns:

Type	Description
HashAddress	File's hash address.

Source code in kiara/utils/hashfs/__init__.py

def get(self, file):
    """Return :class:`HashAdress` from given id or path. If `file` does not
    refer to a valid file, then ``None`` is returned.

    Args:
        file (str): Address ID or path of file.

    Returns:
        HashAddress: File's hash address.
    """
    realpath = self.realpath(file)

    if realpath is None:
        return None
    else:
        return HashAddress(self.unshard(realpath), self.relpath(realpath), realpath)

haspath(self, path)

Return whether path is a subdirectory of the :attr:root directory.

Source code in kiara/utils/hashfs/__init__.py

def haspath(self, path):
    """Return whether `path` is a subdirectory of the :attr:`root`
    directory.
    """
    return issubdir(path, self.root)

idpath(self, id)

Build the file path for a given hash id. Optionally, append a file extension.

Source code in kiara/utils/hashfs/__init__.py

def idpath(self, id):
    """Build the file path for a given hash id. Optionally, append a
    file extension.
    """
    paths = self.shard(id)

    return os.path.join(self.root, *paths)

makepath(self, path)

Physically create the folder path on disk.

Source code in kiara/utils/hashfs/__init__.py

def makepath(self, path):
    """Physically create the folder path on disk."""
    try:
        os.makedirs(path, self.dmode)
    except FileExistsError:
        assert os.path.isdir(path), "expected {} to be a directory".format(path)

open(self, file, mode='rb')

Return open buffer object from given id or path.

Parameters:

Name	Type	Description	Default
file	str	Address ID or path of file.	required
mode	str	Mode to open file in. Defaults to 'rb'.	'rb'

Returns:

Type	Description
Buffer	An io buffer dependent on the mode.

Exceptions:

Type	Description
IOError	If file doesn't exist.

Source code in kiara/utils/hashfs/__init__.py

def open(self, file, mode="rb"):
    """Return open buffer object from given id or path.

    Args:
        file (str): Address ID or path of file.
        mode (str, optional): Mode to open file in. Defaults to ``'rb'``.

    Returns:
        Buffer: An ``io`` buffer dependent on the `mode`.

    Raises:
        IOError: If file doesn't exist.
    """
    realpath = self.realpath(file)
    if realpath is None:
        raise IOError("Could not locate file: {0}".format(file))

    return io.open(realpath, mode)

put(self, file)

Store contents of file on disk using its content hash for the address.

Parameters:

Name	Type	Description	Default
file	mixed	Readable object or path to file.	required

Returns:

Type	Description
HashAddress	File's hash address.

Source code in kiara/utils/hashfs/__init__.py

def put(self, file: BinaryIO) -> "HashAddress":
    """Store contents of `file` on disk using its content hash for the
    address.

    Args:
        file (mixed): Readable object or path to file.

    Returns:
        HashAddress: File's hash address.
    """
    stream = Stream(file)

    with closing(stream):
        id = self.computehash(stream)
        filepath, is_duplicate = self._copy(stream, id)

    return HashAddress(id, self.relpath(filepath), filepath, is_duplicate)

put_with_precomputed_hash(self, file, hash_id)

Source code in kiara/utils/hashfs/__init__.py

def put_with_precomputed_hash(
    self, file: Union[str, Path, BinaryIO], hash_id: str
) -> "HashAddress":

    stream = Stream(file)
    with closing(stream):
        filepath, is_duplicate = self._copy(stream=stream, id=hash_id)

    return HashAddress(hash_id, self.relpath(filepath), filepath, is_duplicate)

realpath(self, file)

Attempt to determine the real path of a file id or path through successive checking of candidate paths. If the real path is stored with an extension, the path is considered a match if the basename matches the expected file path of the id.

Source code in kiara/utils/hashfs/__init__.py

def realpath(self, file):
    """Attempt to determine the real path of a file id or path through
    successive checking of candidate paths. If the real path is stored with
    an extension, the path is considered a match if the basename matches
    the expected file path of the id.
    """
    # Check for absoluate path.
    if os.path.isfile(file):
        return file

    # Check for relative path.
    relpath = os.path.join(self.root, file)
    if os.path.isfile(relpath):
        return relpath

    # Check for sharded path.
    filepath = self.idpath(file)
    if os.path.isfile(filepath):
        return filepath

    # Check for sharded path with any extension.
    paths = glob.glob("{0}.*".format(filepath))
    if paths:
        return paths[0]

    # Could not determine a match.
    return None

relpath(self, path)

Return path relative to the :attr:root directory.

Source code in kiara/utils/hashfs/__init__.py

def relpath(self, path):
    """Return `path` relative to the :attr:`root` directory."""
    return os.path.relpath(path, self.root)

remove_empty(self, subpath)

Successively remove all empty folders starting with subpath and proceeding "up" through directory tree until reaching the :attr:root folder.

Source code in kiara/utils/hashfs/__init__.py

def remove_empty(self, subpath):
    """Successively remove all empty folders starting with `subpath` and
    proceeding "up" through directory tree until reaching the :attr:`root`
    folder.
    """
    # Don't attempt to remove any folders if subpath is not a
    # subdirectory of the root directory.
    if not self.haspath(subpath):
        return

    while subpath != self.root:
        if len(os.listdir(subpath)) > 0 or os.path.islink(subpath):
            break
        os.rmdir(subpath)
        subpath = os.path.dirname(subpath)

repair(self)

Repair any file locations whose content address doesn't match it's file path.

Source code in kiara/utils/hashfs/__init__.py

def repair(self):
    """Repair any file locations whose content address doesn't match it's
    file path.
    """
    repaired = []
    corrupted = tuple(self.corrupted())
    oldmask = os.umask(0)

    try:
        for path, address in corrupted:
            if os.path.isfile(address.abspath):
                # File already exists so just delete corrupted path.
                os.remove(path)
            else:
                # File doesn't exists so move it.
                self.makepath(os.path.dirname(address.abspath))
                shutil.move(path, address.abspath)

            os.chmod(address.abspath, self.fmode)
            repaired.append((path, address))
    finally:
        os.umask(oldmask)

    return repaired

shard(self, id)

Shard content ID into subfolders.

Source code in kiara/utils/hashfs/__init__.py

def shard(self, id):
    """Shard content ID into subfolders."""
    return shard(id, self.depth, self.width)

size(self)

Return the total size in bytes of all files in the :attr:root directory.

Source code in kiara/utils/hashfs/__init__.py

def size(self):
    """Return the total size in bytes of all files in the :attr:`root`
    directory.
    """
    total = 0

    for path in self.files():
        total += os.path.getsize(path)

    return total

unshard(self, path)

Unshard path to determine hash value.

Source code in kiara/utils/hashfs/__init__.py

def unshard(self, path):
    """Unshard path to determine hash value."""
    if not self.haspath(path):
        raise ValueError(
            "Cannot unshard path. The path {0!r} is not "
            "a subdirectory of the root directory {1!r}".format(path, self.root)
        )

    return os.path.splitext(self.relpath(path))[0].replace(os.sep, "")

Stream

Common interface for file-like objects.

The input obj can be a file-like object or a path to a file. If obj is a path to a file, then it will be opened until :meth:close is called. If obj is a file-like object, then it's original position will be restored when :meth:close is called instead of closing the object automatically. Closing of the stream is deferred to whatever process passed the stream in.

Successive readings of the stream is supported without having to manually set it's position back to 0.

Source code in kiara/utils/hashfs/__init__.py

class Stream(object):
    """Common interface for file-like objects.

    The input `obj` can be a file-like object or a path to a file. If `obj` is
    a path to a file, then it will be opened until :meth:`close` is called.
    If `obj` is a file-like object, then it's original position will be
    restored when :meth:`close` is called instead of closing the object
    automatically. Closing of the stream is deferred to whatever process passed
    the stream in.

    Successive readings of the stream is supported without having to manually
    set it's position back to ``0``.
    """

    def __init__(self, obj: Union[BinaryIO, str, Path]):
        if hasattr(obj, "read"):
            pos = obj.tell()  # type: ignore
        elif os.path.isfile(obj):  # type: ignore
            obj = io.open(obj, "rb")  # type: ignore
            pos = None
        else:
            raise ValueError("Object must be a valid file path or a readable object")

        try:
            file_stat = os.stat(obj.name)  # type: ignore
            buffer_size = file_stat.st_blksize
        except Exception:
            buffer_size = 8192

        self._obj = obj
        self._pos = pos
        self._buffer_size = buffer_size

    def __iter__(self):
        """Read underlying IO object and yield results. Return object to
        original position if we didn't open it originally.
        """
        self._obj.seek(0)

        while True:
            data = self._obj.read(self._buffer_size)

            if not data:
                break

            yield data

        if self._pos is not None:
            self._obj.seek(self._pos)

    def close(self):
        """Close underlying IO object if we opened it, else return it to
        original position.
        """
        if self._pos is None:
            self._obj.close()
        else:
            self._obj.seek(self._pos)

Methods

close(self)

Close underlying IO object if we opened it, else return it to original position.

Source code in kiara/utils/hashfs/__init__.py

def close(self):
    """Close underlying IO object if we opened it, else return it to
    original position.
    """
    if self._pos is None:
        self._obj.close()
    else:
        self._obj.seek(self._pos)

Functions

compact(items)

Return only truthy elements of items.

Source code in kiara/utils/hashfs/__init__.py

def compact(items: List[Any]) -> List[Any]:
    """Return only truthy elements of `items`."""
    return [item for item in items if item]

issubdir(subpath, path)

Return whether subpath is a sub-directory of path.

Source code in kiara/utils/hashfs/__init__.py

def issubdir(subpath: str, path: str):
    """Return whether `subpath` is a sub-directory of `path`."""
    # Append os.sep so that paths like /usr/var2/log doesn't match /usr/var.
    path = os.path.realpath(path) + os.sep
    subpath = os.path.realpath(subpath)
    return subpath.startswith(path)

shard(digest, depth, width)

Source code in kiara/utils/hashfs/__init__.py

def shard(digest, depth, width):
    # This creates a list of `depth` number of tokens with width
    # `width` from the first part of the id plus the remainder.
    return compact(
        [digest[i * width : width * (i + 1)] for i in range(depth)]  # noqa
        + [digest[depth * width :]]  # noqa
    )

to_bytes(text)

Source code in kiara/utils/hashfs/__init__.py

def to_bytes(text: Union[str, bytes]):
    if not isinstance(text, bytes):
        text = bytes(text, "utf8")
    return text

hashing

compute_cid(data, hash_codec='sha2-256', encode='base58btc')

Source code in kiara/utils/hashing.py

def compute_cid(
    data: EncodableType,
    hash_codec: str = "sha2-256",
    encode: str = "base58btc",
) -> Tuple[bytes, CID]:

    encoded = dag_cbor.encode(data)
    hash_func = multihash.get(hash_codec)
    digest = hash_func.digest(encoded)

    cid = CID(encode, 1, codec="dag-cbor", digest=digest)
    return encoded, cid

compute_cid_from_file(file, codec='raw', hash_codec='sha2-256')

Source code in kiara/utils/hashing.py

def compute_cid_from_file(
    file: str, codec: Union[str, int, Multicodec] = "raw", hash_codec: str = "sha2-256"
):

    assert hash_codec == "sha2-256"

    hash_func = hashlib.sha256
    file_hash = hash_func()

    CHUNK_SIZE = 65536
    with open(file, "rb") as f:
        fb = f.read(CHUNK_SIZE)
        while len(fb) > 0:
            file_hash.update(fb)
            fb = f.read(CHUNK_SIZE)

    wrapped = multihash.wrap(file_hash.digest(), "sha2-256")
    return create_cid_digest(digest=wrapped, codec=codec)

create_cid_digest(digest, codec='raw')

Source code in kiara/utils/hashing.py

def create_cid_digest(
    digest: Union[
        str, BytesLike, Tuple[Union[str, int, Multihash], Union[str, BytesLike]]
    ],
    codec: Union[str, int, Multicodec] = "raw",
) -> CID:

    cid = CID("base58btc", 1, codec, digest)
    return cid

jupyter

create_image(graph)

Source code in kiara/utils/jupyter.py

def create_image(graph: nx.Graph):

    try:
        import pygraphviz as pgv  # noqa
    except:  # noqa
        return "pygraphviz not available, please install it manually into the current virtualenv"

    # graph = nx.relabel_nodes(graph, lambda x: hash(x))
    G = nx.nx_agraph.to_agraph(graph)

    G.node_attr["shape"] = "box"
    # G.unflatten().layout(prog="dot")
    G.layout(prog="dot")

    b = G.draw(format="png")
    return b

graph_to_image(graph, return_bytes=False)

Source code in kiara/utils/jupyter.py

def graph_to_image(graph: nx.Graph, return_bytes: bool = False):
    b = create_image(graph=graph)
    if return_bytes:
        return b
    else:
        return Image(b)

save_image(graph, path)

Source code in kiara/utils/jupyter.py

def save_image(graph: nx.Graph, path: str):

    graph_b = create_image(graph=graph)
    with open(path, "wb") as f:
        f.write(graph_b)

metadata

find_metadata_models(alias=None, only_for_package=None)

Source code in kiara/utils/metadata.py

def find_metadata_models(
    alias: Optional[str] = None, only_for_package: Optional[str] = None
) -> MetadataTypeClassesInfo:

    model_registry = ModelRegistry.instance()
    _group = model_registry.get_models_of_type(ValueMetadata)  # type: ignore

    classes: Dict[str, Type[ValueMetadata]] = {}
    for model_id, info in _group.items():
        classes[model_id] = info.python_class.get_class()  # type: ignore

    group: MetadataTypeClassesInfo = MetadataTypeClassesInfo.create_from_type_items(group_alias=alias, **classes)  # type: ignore

    if only_for_package:
        temp = {}
        for key, info in group.items():
            if info.context.labels.get("package") == only_for_package:
                temp[key] = info

        group = MetadataTypeClassesInfo.construct(
            group_id=group.instance_id, group_alias=group.group_alias, type_infos=temp  # type: ignore
        )

    return group

models

Functions

assemble_subcomponent_tree(data)

Source code in kiara/utils/models.py

def assemble_subcomponent_tree(data: "KiaraModel") -> Optional[nx.DiGraph]:

    graph = nx.DiGraph()

    def assemble_tree(info_model: KiaraModel, current_node_id, level: int = 0):
        graph.add_node(current_node_id, obj=info_model, level=level)
        scn = info_model.subcomponent_keys
        if not scn:
            return
        for child_path in scn:
            child_obj = info_model.get_subcomponent(child_path)
            new_node_id = f"{current_node_id}.{child_path}"
            graph.add_edge(current_node_id, new_node_id)
            assemble_tree(child_obj, new_node_id, level + 1)

    assemble_tree(data, KIARA_DEFAULT_ROOT_NODE_ID)
    return graph

create_pydantic_model(model_cls, _use_pydantic_construct=False, **field_values)

Source code in kiara/utils/models.py

def create_pydantic_model(
    model_cls: Type[BaseModel],
    _use_pydantic_construct: bool = PYDANTIC_USE_CONSTRUCT,
    **field_values: Any,
):

    if _use_pydantic_construct:
        raise NotImplementedError()
        return model_cls.construct(**field_values)
    else:
        return model_cls(**field_values)

get_subcomponent_from_model(data, path)

Return subcomponents of a model under a specified path.

Source code in kiara/utils/models.py

def get_subcomponent_from_model(data: "KiaraModel", path: str) -> "KiaraModel":
    """Return subcomponents of a model under a specified path."""

    if "." in path:
        first_token, rest = path.split(".", maxsplit=1)
        sc = data.get_subcomponent(first_token)
        return sc.get_subcomponent(rest)

    if hasattr(data, "__custom_root_type__") and data.__custom_root_type__:
        if isinstance(data.__root__, Mapping):  # type: ignore
            if path in data.__root__.keys():  # type: ignore
                return data.__root__[path]  # type: ignore
            else:
                matches = {}
                for k in data.__root__.keys():  # type: ignore
                    if k.startswith(f"{path}."):
                        rest = k[len(path) + 1 :]  # noqa
                        matches[rest] = data.__root__[k]  # type: ignore

                if not matches:
                    raise KeyError(f"No child models under '{path}'.")
                else:
                    raise NotImplementedError()
                    # subcomponent_group = KiaraModelGroup.create_from_child_models(**matches)
                    # return subcomponent_group

        else:
            raise NotImplementedError()
    else:
        if path in data.__fields__.keys():
            return getattr(data, path)
        else:
            raise KeyError(
                f"No subcomponent for key '{path}' in model: {data.instance_id}."
            )

retrieve_data_subcomponent_keys(data)

Source code in kiara/utils/models.py

def retrieve_data_subcomponent_keys(data: Any) -> Iterable[str]:

    if hasattr(data, "__custom_root_type__") and data.__custom_root_type__:
        if isinstance(data.__root__, Mapping):  # type: ignore
            result = set()
            for k, v in data.__root__.items():  # type: ignore
                if isinstance(v, BaseModel):
                    result.add(k.split(".")[0])
            return result
        else:
            return []
    elif isinstance(data, BaseModel):
        matches = []
        for x in data.__fields__.keys():
            _type = data.__fields__[x].type_
            if isinstance(_type, type) and issubclass(_type, BaseModel):
                matches.append(x)
        return matches
    else:
        log_message(
            f"No subcomponents retrieval supported for data of type: {type(data)}"
        )
        return []

operations

filter_operations(kiara, pkg_name=None, **operations)

Source code in kiara/utils/operations.py

def filter_operations(
    kiara: "Kiara", pkg_name: Optional[str] = None, **operations: "Operation"
) -> OperationGroupInfo:

    result: Dict[str, OperationInfo] = {}

    # op_infos = kiara.operation_registry.get_context_metadata(only_for_package=pkg_name)
    modules = kiara.module_registry.get_context_metadata(only_for_package=pkg_name)

    for op_id, op in operations.items():

        if op.module.module_type_name != "pipeline":
            if op.module.module_type_name in modules.keys():
                result[op_id] = OperationInfo.create_from_operation(
                    kiara=kiara, operation=op
                )
                continue
        else:
            package: Optional[str] = op.metadata.get("labels", {}).get("package", None)
            if not pkg_name or (package and package == pkg_name):
                result[op_id] = OperationInfo.create_from_operation(
                    kiara=kiara, operation=op
                )

        # opt_types = kiara.operation_registry.find_all_operation_types(op_id)
        # match = False
        # for ot in opt_types:
        #     if ot in op_infos.keys():
        #         match = True
        #         break
        #
        # if match:
        #     result[op_id] = OperationInfo.create_from_operation(
        #         kiara=kiara, operation=op
        #     )

    return OperationGroupInfo.construct(type_infos=result)

output

Classes

ArrowTabularWrap (TabularWrap)

Source code in kiara/utils/output.py

class ArrowTabularWrap(TabularWrap):
    def __init__(self, table: "ArrowTable"):
        self._table: "ArrowTable" = table
        super().__init__()

    def retrieve_column_names(self) -> Iterable[str]:
        return self._table.column_names

    def retrieve_number_of_rows(self) -> int:
        return self._table.num_rows

    def slice(self, offset: int = 0, length: Optional[int] = None):
        return self._table.slice(offset=offset, length=length)

    def to_pydict(self) -> Mapping:
        return self._table.to_pydict()

retrieve_column_names(self)

Source code in kiara/utils/output.py

def retrieve_column_names(self) -> Iterable[str]:
    return self._table.column_names

retrieve_number_of_rows(self)

Source code in kiara/utils/output.py

def retrieve_number_of_rows(self) -> int:
    return self._table.num_rows

slice(self, offset=0, length=None)

Source code in kiara/utils/output.py

def slice(self, offset: int = 0, length: Optional[int] = None):
    return self._table.slice(offset=offset, length=length)

to_pydict(self)

Source code in kiara/utils/output.py

def to_pydict(self) -> Mapping:
    return self._table.to_pydict()

DictTabularWrap (TabularWrap)

Source code in kiara/utils/output.py

class DictTabularWrap(TabularWrap):
    def __init__(self, data: Mapping[str, Any]):

        self._data: Mapping[str, Any] = data

    def retrieve_number_of_rows(self) -> int:
        return len(self._data)

    def retrieve_column_names(self) -> Iterable[str]:
        return self._data.keys()

    def to_pydict(self) -> Mapping:
        return self._data

    def slice(self, offset: int = 0, length: Optional[int] = None) -> "TabularWrap":

        result = {}
        start = None
        end = None
        for cn in self._data.keys():
            if start is None:
                if offset > len(self._data):
                    return DictTabularWrap({cn: [] for cn in self._data.keys()})
                start = offset
                if not length:
                    end = len(self._data)
                else:
                    end = start + length
                    if end > len(self._data):
                        end = len(self._data)
            result[cn] = self._data[cn][start:end]
        return DictTabularWrap(result)

retrieve_column_names(self)

Source code in kiara/utils/output.py

def retrieve_column_names(self) -> Iterable[str]:
    return self._data.keys()

retrieve_number_of_rows(self)

Source code in kiara/utils/output.py

def retrieve_number_of_rows(self) -> int:
    return len(self._data)

slice(self, offset=0, length=None)

Source code in kiara/utils/output.py

def slice(self, offset: int = 0, length: Optional[int] = None) -> "TabularWrap":

    result = {}
    start = None
    end = None
    for cn in self._data.keys():
        if start is None:
            if offset > len(self._data):
                return DictTabularWrap({cn: [] for cn in self._data.keys()})
            start = offset
            if not length:
                end = len(self._data)
            else:
                end = start + length
                if end > len(self._data):
                    end = len(self._data)
        result[cn] = self._data[cn][start:end]
    return DictTabularWrap(result)

to_pydict(self)

Source code in kiara/utils/output.py

def to_pydict(self) -> Mapping:
    return self._data

OutputDetails (BaseModel) pydantic-model

Source code in kiara/utils/output.py

class OutputDetails(BaseModel):
    @classmethod
    def from_data(cls, data: Any):

        if isinstance(data, str):
            if "=" in data:
                data = [data]
            else:
                data = [f"format={data}"]

        if isinstance(data, Iterable):
            data = list(data)
            if len(data) == 1 and isinstance(data[0], str) and "=" not in data[0]:
                data = [f"format={data[0]}"]
            output_details_dict = dict_from_cli_args(*data)
        else:
            raise TypeError(
                f"Can't parse output detail config: invalid input type '{type(data)}'."
            )

        output_details = OutputDetails(**output_details_dict)
        return output_details

    format: str = Field(description="The output format.")
    target: str = Field(description="The output target.")
    config: Dict[str, Any] = Field(
        description="Output configuration.", default_factory=dict
    )

    @root_validator(pre=True)
    def _set_defaults(cls, values):

        target: str = values.pop("target", "terminal")
        format: str = values.pop("format", None)
        if format is None:
            if target == "terminal":
                format = "terminal"
            else:
                if target == "file":
                    format = "json"
                else:
                    ext = target.split(".")[-1]
                    if ext in ["yaml", "json"]:
                        format = ext
                    else:
                        format = "json"
        result = {"format": format, "target": target, "config": dict(values)}

        return result

Attributes

config: Dict[str, Any] pydantic-field

Output configuration.

format: str pydantic-field required

The output format.

target: str pydantic-field required

The output target.

from_data(data) classmethod

Source code in kiara/utils/output.py

@classmethod
def from_data(cls, data: Any):

    if isinstance(data, str):
        if "=" in data:
            data = [data]
        else:
            data = [f"format={data}"]

    if isinstance(data, Iterable):
        data = list(data)
        if len(data) == 1 and isinstance(data[0], str) and "=" not in data[0]:
            data = [f"format={data[0]}"]
        output_details_dict = dict_from_cli_args(*data)
    else:
        raise TypeError(
            f"Can't parse output detail config: invalid input type '{type(data)}'."
        )

    output_details = OutputDetails(**output_details_dict)
    return output_details

RenderConfig (BaseModel) pydantic-model

Source code in kiara/utils/output.py

class RenderConfig(BaseModel):

    render_format: str = Field(description="The output format.", default="terminal")

Attributes

render_format: str pydantic-field

The output format.

SqliteTabularWrap (TabularWrap)

Source code in kiara/utils/output.py

class SqliteTabularWrap(TabularWrap):
    def __init__(self, engine: "Engine", table_name: str):
        self._engine: Engine = engine
        self._table_name: str = table_name
        super().__init__()

    def retrieve_number_of_rows(self) -> int:

        from sqlalchemy import text

        with self._engine.connect() as con:
            result = con.execute(text(f"SELECT count(*) from {self._table_name}"))
            num_rows = result.fetchone()[0]

        return num_rows

    def retrieve_column_names(self) -> Iterable[str]:

        from sqlalchemy import inspect

        engine = self._engine
        inspector = inspect(engine)
        columns = inspector.get_columns(self._table_name)
        result = [column["name"] for column in columns]
        return result

    def slice(self, offset: int = 0, length: Optional[int] = None) -> "TabularWrap":

        from sqlalchemy import text

        query = f"SELECT * FROM {self._table_name}"
        if length:
            query = f"{query} LIMIT {length}"
        else:
            query = f"{query} LIMIT {self.num_rows}"
        if offset > 0:
            query = f"{query} OFFSET {offset}"
        with self._engine.connect() as con:
            result = con.execute(text(query))
            result_dict: Dict[str, List[Any]] = {}
            for cn in self.column_names:
                result_dict[cn] = []
            for r in result:
                for i, cn in enumerate(self.column_names):
                    result_dict[cn].append(r[i])

        return DictTabularWrap(result_dict)

    def to_pydict(self) -> Mapping:

        from sqlalchemy import text

        query = f"SELECT * FROM {self._table_name}"

        with self._engine.connect() as con:
            result = con.execute(text(query))
            result_dict: Dict[str, List[Any]] = {}
            for cn in self.column_names:
                result_dict[cn] = []
            for r in result:
                for i, cn in enumerate(self.column_names):
                    result_dict[cn].append(r[i])

        return result_dict

retrieve_column_names(self)

Source code in kiara/utils/output.py

def retrieve_column_names(self) -> Iterable[str]:

    from sqlalchemy import inspect

    engine = self._engine
    inspector = inspect(engine)
    columns = inspector.get_columns(self._table_name)
    result = [column["name"] for column in columns]
    return result

retrieve_number_of_rows(self)

Source code in kiara/utils/output.py

def retrieve_number_of_rows(self) -> int:

    from sqlalchemy import text

    with self._engine.connect() as con:
        result = con.execute(text(f"SELECT count(*) from {self._table_name}"))
        num_rows = result.fetchone()[0]

    return num_rows

slice(self, offset=0, length=None)

Source code in kiara/utils/output.py

def slice(self, offset: int = 0, length: Optional[int] = None) -> "TabularWrap":

    from sqlalchemy import text

    query = f"SELECT * FROM {self._table_name}"
    if length:
        query = f"{query} LIMIT {length}"
    else:
        query = f"{query} LIMIT {self.num_rows}"
    if offset > 0:
        query = f"{query} OFFSET {offset}"
    with self._engine.connect() as con:
        result = con.execute(text(query))
        result_dict: Dict[str, List[Any]] = {}
        for cn in self.column_names:
            result_dict[cn] = []
        for r in result:
            for i, cn in enumerate(self.column_names):
                result_dict[cn].append(r[i])

    return DictTabularWrap(result_dict)

to_pydict(self)

Source code in kiara/utils/output.py

def to_pydict(self) -> Mapping:

    from sqlalchemy import text

    query = f"SELECT * FROM {self._table_name}"

    with self._engine.connect() as con:
        result = con.execute(text(query))
        result_dict: Dict[str, List[Any]] = {}
        for cn in self.column_names:
            result_dict[cn] = []
        for r in result:
            for i, cn in enumerate(self.column_names):
                result_dict[cn].append(r[i])

    return result_dict

TabularWrap (ABC)

Source code in kiara/utils/output.py

class TabularWrap(ABC):
    def __init__(self):
        self._num_rows: Optional[int] = None
        self._column_names: Optional[Iterable[str]] = None

    @property
    def num_rows(self) -> int:
        if self._num_rows is None:
            self._num_rows = self.retrieve_number_of_rows()
        return self._num_rows

    @property
    def column_names(self) -> Iterable[str]:
        if self._column_names is None:
            self._column_names = self.retrieve_column_names()
        return self._column_names

    @abstractmethod
    def retrieve_column_names(self) -> Iterable[str]:
        pass

    @abstractmethod
    def retrieve_number_of_rows(self) -> int:
        pass

    @abstractmethod
    def slice(self, offset: int = 0, length: Optional[int] = None) -> "TabularWrap":
        pass

    @abstractmethod
    def to_pydict(self) -> Mapping:
        pass

    def pretty_print(
        self,
        rows_head: Optional[int] = None,
        rows_tail: Optional[int] = None,
        max_row_height: Optional[int] = None,
        max_cell_length: Optional[int] = None,
        show_table_header: bool = True,
    ) -> RenderableType:

        rich_table = RichTable(box=box.SIMPLE, show_header=show_table_header)
        for cn in self.retrieve_column_names():
            rich_table.add_column(cn)

        num_split_rows = 2

        if rows_head is not None:

            if rows_head < 0:
                rows_head = 0

            if rows_head > self.retrieve_number_of_rows():
                rows_head = self.retrieve_number_of_rows()
                rows_tail = None
                num_split_rows = 0

            if rows_tail is not None:
                if rows_head + rows_tail >= self.num_rows:  # type: ignore
                    rows_head = self.retrieve_number_of_rows()
                    rows_tail = None
                    num_split_rows = 0
        else:
            num_split_rows = 0

        if rows_head is not None:
            head = self.slice(0, rows_head)
            num_rows = rows_head
        else:
            head = self
            num_rows = self.retrieve_number_of_rows()

        table_dict = head.to_pydict()
        for i in range(0, num_rows):
            row = []
            for cn in self.retrieve_column_names():
                cell = table_dict[cn][i]
                cell_str = str(cell)
                if max_row_height and max_row_height > 0 and "\n" in cell_str:
                    lines = cell_str.split("\n")
                    if len(lines) > max_row_height:
                        if max_row_height == 1:
                            lines = lines[0:1]
                        else:
                            half = int(max_row_height / 2)
                            lines = lines[0:half] + [".."] + lines[-half:]
                    cell_str = "\n".join(lines)

                if max_cell_length and max_cell_length > 0:
                    lines = []
                    for line in cell_str.split("\n"):
                        if len(line) > max_cell_length:
                            line = line[0:max_cell_length] + " ..."
                        else:
                            line = line
                        lines.append(line)
                    cell_str = "\n".join(lines)

                row.append(cell_str)

            rich_table.add_row(*row)

        if num_split_rows:
            for i in range(0, num_split_rows):
                row = []
                for _ in self.retrieve_column_names():
                    row.append("...")
                rich_table.add_row(*row)

        if rows_head:
            if rows_tail is not None:
                if rows_tail < 0:
                    rows_tail = 0

                tail = self.slice(self.retrieve_number_of_rows() - rows_tail)
                table_dict = tail.to_pydict()
                for i in range(0, num_rows):

                    row = []
                    for cn in self.retrieve_column_names():

                        cell = table_dict[cn][i]
                        cell_str = str(cell)

                        if max_row_height and max_row_height > 0 and "\n" in cell_str:
                            lines = cell_str.split("\n")
                            if len(lines) > max_row_height:
                                if max_row_height == 1:
                                    lines = lines[0:1]
                                else:
                                    half = int(len(lines) / 2)
                                    lines = lines[0:half] + [".."] + lines[-half:]
                            cell_str = "\n".join(lines)

                        if max_cell_length and max_cell_length > 0:
                            lines = []
                            for line in cell_str.split("\n"):

                                if len(line) > max_cell_length:
                                    line = line[0:(max_cell_length)] + " ..."
                                else:
                                    line = line
                                lines.append(line)
                            cell_str = "\n".join(lines)

                        row.append(cell_str)

                    rich_table.add_row(*row)

        return rich_table

column_names: Iterable[str] property readonly

num_rows: int property readonly

pretty_print(self, rows_head=None, rows_tail=None, max_row_height=None, max_cell_length=None, show_table_header=True)

Source code in kiara/utils/output.py

def pretty_print(
    self,
    rows_head: Optional[int] = None,
    rows_tail: Optional[int] = None,
    max_row_height: Optional[int] = None,
    max_cell_length: Optional[int] = None,
    show_table_header: bool = True,
) -> RenderableType:

    rich_table = RichTable(box=box.SIMPLE, show_header=show_table_header)
    for cn in self.retrieve_column_names():
        rich_table.add_column(cn)

    num_split_rows = 2

    if rows_head is not None:

        if rows_head < 0:
            rows_head = 0

        if rows_head > self.retrieve_number_of_rows():
            rows_head = self.retrieve_number_of_rows()
            rows_tail = None
            num_split_rows = 0

        if rows_tail is not None:
            if rows_head + rows_tail >= self.num_rows:  # type: ignore
                rows_head = self.retrieve_number_of_rows()
                rows_tail = None
                num_split_rows = 0
    else:
        num_split_rows = 0

    if rows_head is not None:
        head = self.slice(0, rows_head)
        num_rows = rows_head
    else:
        head = self
        num_rows = self.retrieve_number_of_rows()

    table_dict = head.to_pydict()
    for i in range(0, num_rows):
        row = []
        for cn in self.retrieve_column_names():
            cell = table_dict[cn][i]
            cell_str = str(cell)
            if max_row_height and max_row_height > 0 and "\n" in cell_str:
                lines = cell_str.split("\n")
                if len(lines) > max_row_height:
                    if max_row_height == 1:
                        lines = lines[0:1]
                    else:
                        half = int(max_row_height / 2)
                        lines = lines[0:half] + [".."] + lines[-half:]
                cell_str = "\n".join(lines)

            if max_cell_length and max_cell_length > 0:
                lines = []
                for line in cell_str.split("\n"):
                    if len(line) > max_cell_length:
                        line = line[0:max_cell_length] + " ..."
                    else:
                        line = line
                    lines.append(line)
                cell_str = "\n".join(lines)

            row.append(cell_str)

        rich_table.add_row(*row)

    if num_split_rows:
        for i in range(0, num_split_rows):
            row = []
            for _ in self.retrieve_column_names():
                row.append("...")
            rich_table.add_row(*row)

    if rows_head:
        if rows_tail is not None:
            if rows_tail < 0:
                rows_tail = 0

            tail = self.slice(self.retrieve_number_of_rows() - rows_tail)
            table_dict = tail.to_pydict()
            for i in range(0, num_rows):

                row = []
                for cn in self.retrieve_column_names():

                    cell = table_dict[cn][i]
                    cell_str = str(cell)

                    if max_row_height and max_row_height > 0 and "\n" in cell_str:
                        lines = cell_str.split("\n")
                        if len(lines) > max_row_height:
                            if max_row_height == 1:
                                lines = lines[0:1]
                            else:
                                half = int(len(lines) / 2)
                                lines = lines[0:half] + [".."] + lines[-half:]
                        cell_str = "\n".join(lines)

                    if max_cell_length and max_cell_length > 0:
                        lines = []
                        for line in cell_str.split("\n"):

                            if len(line) > max_cell_length:
                                line = line[0:(max_cell_length)] + " ..."
                            else:
                                line = line
                            lines.append(line)
                        cell_str = "\n".join(lines)

                    row.append(cell_str)

                rich_table.add_row(*row)

    return rich_table

retrieve_column_names(self)

Source code in kiara/utils/output.py

@abstractmethod
def retrieve_column_names(self) -> Iterable[str]:
    pass

retrieve_number_of_rows(self)

Source code in kiara/utils/output.py

@abstractmethod
def retrieve_number_of_rows(self) -> int:
    pass

slice(self, offset=0, length=None)

Source code in kiara/utils/output.py

@abstractmethod
def slice(self, offset: int = 0, length: Optional[int] = None) -> "TabularWrap":
    pass

to_pydict(self)

Source code in kiara/utils/output.py

@abstractmethod
def to_pydict(self) -> Mapping:
    pass

Functions

create_renderable_from_values(values, config=None)

Create a renderable for this module configuration.

Source code in kiara/utils/output.py

def create_renderable_from_values(
    values: Mapping[str, "Value"], config: Optional[Mapping[str, Any]] = None
) -> RenderableType:
    """Create a renderable for this module configuration."""

    if config is None:
        config = {}

    render_format = config.get("render_format", "terminal")
    if render_format not in ["terminal"]:
        raise Exception(f"Invalid render format: {render_format}")

    show_pedigree = config.get("show_pedigree", False)
    show_data = config.get("show_data", False)
    show_hash = config.get("show_hash", True)
    # show_load_config = config.get("show_load_config", False)

    table = RichTable(show_lines=True, box=box.MINIMAL_DOUBLE_HEAD)
    table.add_column("value_id", "i")
    table.add_column("data_type")
    table.add_column("size")
    if show_hash:
        table.add_column("hash")
    if show_pedigree:
        table.add_column("pedigree")
    if show_data:
        table.add_column("data")

    for id, value in sorted(values.items(), key=lambda item: item[1].value_schema.type):
        row: List[RenderableType] = [id, value.value_schema.type, str(value.value_size)]
        if show_hash:
            row.append(str(value.value_hash))
        if show_pedigree:
            if value.pedigree == ORPHAN:
                pedigree = "-- n/a --"
            else:
                pedigree = value.pedigree.json(option=orjson.OPT_INDENT_2)
            row.append(pedigree)
        if show_data:
            data = value._data_registry.pretty_print_data(
                value_id=value.value_id, target_type="terminal_renderable", **config
            )
            row.append(data)
        # if show_load_config:
        #     load_config = value.retrieve_load_config()
        #     if load_config is None:
        #         load_config_str: RenderableType = "-- not stored (yet) --"
        #     else:
        #         load_config_str = load_config.create_renderable()
        #     row.append(load_config_str)
        table.add_row(*row)

    return table

create_table_from_base_model_cls(model_cls)

Source code in kiara/utils/output.py

def create_table_from_base_model_cls(model_cls: Type[BaseModel]):

    table = RichTable(box=box.SIMPLE, show_lines=True)
    table.add_column("Field")
    table.add_column("Type")
    table.add_column("Description")
    table.add_column("Required")
    table.add_column("Default")

    props = model_cls.schema().get("properties", {})

    for field_name, field in sorted(model_cls.__fields__.items()):
        row = [field_name]
        p = props.get(field_name, None)
        p_type = None
        if p is not None:
            p_type = p.get("type", None)
            # TODO: check 'anyOf' keys

        if p_type is None:
            p_type = "-- check source --"
        row.append(p_type)
        desc = p.get("description", "")
        row.append(desc)
        row.append("yes" if field.required else "no")
        default = field.default
        if callable(default):
            default = default()

        if default is None:
            default = ""
        else:
            try:
                default = json.dumps(default, indent=2)
            except Exception:
                default = str(default)
        row.append(default)
        table.add_row(*row)

    return table

create_table_from_field_schemas(_add_default=True, _add_required=True, _show_header=False, _constants=None, **fields)

Source code in kiara/utils/output.py

def create_table_from_field_schemas(
    _add_default: bool = True,
    _add_required: bool = True,
    _show_header: bool = False,
    _constants: Optional[Mapping[str, Any]] = None,
    **fields: "ValueSchema",
) -> RichTable:

    table = RichTable(box=box.SIMPLE, show_header=_show_header)
    table.add_column("field name", style="i")
    table.add_column("type")
    table.add_column("description")

    if _add_required:
        table.add_column("Required")
    if _add_default:
        if _constants:
            table.add_column("Default / Constant")
        else:
            table.add_column("Default")

    for field_name, schema in fields.items():

        row: List[RenderableType] = [field_name, schema.type, schema.doc]

        if _add_required:
            req = schema.is_required()
            if not req:
                req_str = "no"
            else:
                if schema.default in [
                    None,
                    SpecialValue.NO_VALUE,
                    SpecialValue.NOT_SET,
                ]:
                    req_str = "[b]yes[b]"
                else:
                    req_str = "no"
            row.append(req_str)

        if _add_default:
            if _constants and field_name in _constants.keys():
                d = f"[b]{_constants[field_name]}[/b] (constant)"
            else:
                if schema.default in [
                    None,
                    SpecialValue.NO_VALUE,
                    SpecialValue.NOT_SET,
                ]:
                    d = "-- no default --"
                else:
                    d = str(schema.default)
            row.append(d)

        table.add_row(*row)

    return table

create_table_from_model_object(model, render_config=None, exclude_fields=None)

Source code in kiara/utils/output.py

def create_table_from_model_object(
    model: BaseModel,
    render_config: Optional[Mapping[str, Any]] = None,
    exclude_fields: Optional[Set[str]] = None,
):

    model_cls = model.__class__

    table = RichTable(box=box.SIMPLE, show_lines=True)
    table.add_column("Field")
    table.add_column("Type")
    table.add_column("Value")
    table.add_column("Description")

    props = model_cls.schema().get("properties", {})

    for field_name, field in sorted(model_cls.__fields__.items()):
        if exclude_fields and field_name in exclude_fields:
            continue
        row = [field_name]

        p = props.get(field_name, None)
        p_type = None
        if p is not None:
            p_type = p.get("type", None)
            # TODO: check 'anyOf' keys

        if p_type is None:
            p_type = "-- check source --"
        row.append(p_type)

        data = getattr(model, field_name)
        row.append(extract_renderable(data, render_config=render_config))

        desc = p.get("description", "")
        row.append(desc)
        table.add_row(*row)

    return table

create_value_map_status_renderable(inputs, render_config=None)

Source code in kiara/utils/output.py

def create_value_map_status_renderable(
    inputs: ValueMap, render_config: Optional[Mapping[str, Any]] = None
) -> RichTable:

    if render_config is None:
        render_config = {}

    show_required: bool = render_config.get("show_required", True)
    show_default: bool = render_config.get("show_default", True)

    table = RichTable(box=box.SIMPLE, show_header=True)
    table.add_column("field name", style="i")
    table.add_column("status", style="b")
    table.add_column("type")
    table.add_column("description")

    if show_required:
        table.add_column("required")

    if show_default:
        table.add_column("default")

    invalid = inputs.check_invalid()

    for field_name, value in inputs.items():

        row: List[RenderableType] = [field_name]

        if field_name in invalid.keys():
            row.append(f"[red]{invalid[field_name]}[/red]")
        else:
            row.append("[green]valid[/green]")

        value_schema = inputs.values_schema[field_name]

        row.extend([value_schema.type, value_schema.doc.description])

        if show_required:
            req = value_schema.is_required()
            if not req:
                req_str = "no"
            else:
                if value_schema.default in [
                    None,
                    SpecialValue.NO_VALUE,
                    SpecialValue.NOT_SET,
                ]:
                    req_str = "[b]yes[b]"
                else:
                    req_str = "no"
            row.append(req_str)

        if show_default:
            default = value_schema.default
            if callable(default):
                default_val = default()
            else:
                default_val = default

            if default_val in [None, SpecialValue.NOT_SET, SpecialValue.NO_VALUE]:
                default_str = ""
            else:
                default_str = str(default_val)

            row.append(default_str)

        table.add_row(*row)

    return table

extract_renderable(item, render_config=None)

Try to automatically find and extract or create an object that is renderable by the 'rich' library.

Source code in kiara/utils/output.py

def extract_renderable(item: Any, render_config: Optional[Mapping[str, Any]] = None):
    """Try to automatically find and extract or create an object that is renderable by the 'rich' library."""

    if render_config is None:
        render_config = {}
    else:
        render_config = dict(render_config)

    inline_models_as_json = render_config.setdefault("inline_models_as_json", True)

    if hasattr(item, "create_renderable"):
        return item.create_renderable(**render_config)
    elif isinstance(item, (ConsoleRenderable, RichCast, str)):
        return item
    elif isinstance(item, BaseModel) and not inline_models_as_json:
        return create_table_from_model_object(item)
    elif isinstance(item, BaseModel):
        return item.json(indent=2)
    elif isinstance(item, Mapping) and not inline_models_as_json:
        table = RichTable(show_header=False, box=box.SIMPLE)
        table.add_column("Key", style="i")
        table.add_column("Value")
        for k, v in item.items():
            table.add_row(k, extract_renderable(v, render_config=render_config))
        return table
    elif isinstance(item, Mapping):
        result = {}
        for k, v in item.items():
            if isinstance(v, BaseModel):
                v = v.dict()
            result[k] = v
        return orjson_dumps(
            result, option=orjson.OPT_INDENT_2 | orjson.OPT_NON_STR_KEYS
        )
    elif isinstance(item, Iterable):
        _all = []
        for i in item:
            _all.append(extract_renderable(i))
        rg = Group(*_all)
        return rg
    else:
        return str(item)

pipelines

Functions

check_doc_sidecar(path, data)

Source code in kiara/utils/pipelines.py

def check_doc_sidecar(
    path: Union[Path, str], data: Mapping[str, Any]
) -> Mapping[str, Any]:

    if isinstance(path, str):
        path = Path(os.path.expanduser(path))

    _doc = data["data"].get("documentation", None)
    if _doc is None:
        _doc_path = Path(path.as_posix() + ".md")
        if _doc_path.is_file():
            doc = _doc_path.read_text()
            if doc:
                data["data"]["documentation"] = doc

    return data

create_step_value_address(value_address_config, default_field_name)

Source code in kiara/utils/pipelines.py

def create_step_value_address(
    value_address_config: Union[str, Mapping[str, Any]],
    default_field_name: str,
) -> "StepValueAddress":

    if isinstance(value_address_config, StepValueAddress):
        return value_address_config

    sub_value: Optional[Mapping[str, Any]] = None

    if isinstance(value_address_config, str):

        tokens = value_address_config.split(".")
        if len(tokens) == 1:
            step_id = value_address_config
            output_name = default_field_name
        elif len(tokens) == 2:
            step_id = tokens[0]
            output_name = tokens[1]
        elif len(tokens) == 3:
            step_id = tokens[0]
            output_name = tokens[1]
            sub_value = {"config": tokens[2]}
        else:
            raise NotImplementedError()

    elif isinstance(value_address_config, Mapping):

        step_id = value_address_config["step_id"]
        output_name = value_address_config["value_name"]
        sub_value = value_address_config.get("sub_value", None)
    else:
        raise TypeError(
            f"Invalid type for creating step value address: {type(value_address_config)}"
        )

    if sub_value is not None and not isinstance(sub_value, Mapping):
        raise ValueError(
            f"Invalid type '{type(sub_value)}' for sub_value (step_id: {step_id}, value name: {output_name}): {sub_value}"
        )

    input_link = StepValueAddress(
        step_id=step_id, value_name=output_name, sub_value=sub_value
    )
    return input_link

ensure_step_value_addresses(link, default_field_name)

Source code in kiara/utils/pipelines.py

def ensure_step_value_addresses(
    link: Union[str, Mapping, Iterable], default_field_name: str
) -> List["StepValueAddress"]:

    if isinstance(link, (str, Mapping)):
        input_links: List[StepValueAddress] = [
            create_step_value_address(
                value_address_config=link, default_field_name=default_field_name
            )
        ]

    elif isinstance(link, Iterable):
        input_links = []
        for o in link:
            il = create_step_value_address(
                value_address_config=o, default_field_name=default_field_name
            )
            input_links.append(il)
    else:
        raise TypeError(f"Can't parse input map, invalid type for output: {link}")

    return input_links

get_pipeline_details_from_path(path, module_type_name=None, base_module=None)

Load a pipeline description, save it's content, and determine it the pipeline base name.

Parameters:

Name	Type	Description	Default
path	Union[str, pathlib.Path]	the path to the pipeline file	required
module_type_name	Optional[str]	if specifies, overwrites any auto-detected or assigned pipeline name	None
base_module	Optional[str]	overrides the base module the assembled pipeline module will be located in the python hierarchy	None

Source code in kiara/utils/pipelines.py

def get_pipeline_details_from_path(
    path: Union[str, Path],
    module_type_name: Optional[str] = None,
    base_module: Optional[str] = None,
) -> Mapping[str, Any]:
    """Load a pipeline description, save it's content, and determine it the pipeline base name.

    Arguments:
        path: the path to the pipeline file
        module_type_name: if specifies, overwrites any auto-detected or assigned pipeline name
        base_module: overrides the base module the assembled pipeline module will be located in the python hierarchy

    """

    if isinstance(path, str):
        path = Path(os.path.expanduser(path))

    if not path.is_file():
        raise Exception(
            f"Can't add pipeline description '{path.as_posix()}': not a file"
        )

    data = get_data_from_file(path)

    if not data:
        raise Exception(
            f"Can't register pipeline file '{path.as_posix()}': no content."
        )

    if module_type_name:
        data[MODULE_TYPE_NAME_KEY] = module_type_name

    if not isinstance(data, Mapping):
        raise Exception("Not a dictionary type.")

    # filename = path.name
    # name = data.get(MODULE_TYPE_NAME_KEY, None)
    # if name is None:
    #     name = filename.split(".", maxsplit=1)[0]

    result = {"data": data, "source": path.as_posix(), "source_type": "file"}
    if base_module:
        result["base_module"] = base_module
    return result

values

augment_values(values, schemas, constants=None)

Source code in kiara/utils/values.py

def augment_values(
    values: Mapping[str, Any],
    schemas: Mapping[str, ValueSchema],
    constants: Optional[Mapping[str, Any]] = None,
) -> Dict[str, Any]:

    # TODO: check if extra fields were provided

    if constants:
        for k, v in constants.items():
            if k in values.keys():
                raise Exception(f"Invalid input: value provided for constant '{k}'")

    values_new = {}
    for field_name, schema in schemas.items():

        if field_name not in values.keys():
            if constants and field_name in constants:
                values_new[field_name] = copy.deepcopy(constants[field_name])
            elif schema.default != SpecialValue.NOT_SET:
                if callable(schema.default):
                    values_new[field_name] = schema.default()
                else:
                    values_new[field_name] = copy.deepcopy(schema.default)
            else:
                values_new[field_name] = SpecialValue.NOT_SET
        else:
            value = values[field_name]
            if value is None:
                value = SpecialValue.NO_VALUE
            values_new[field_name] = value

    return values_new

create_schema_dict(schema_config)

Source code in kiara/utils/values.py

def create_schema_dict(
    schema_config: Mapping[str, Union[ValueSchema, Mapping[str, Any]]],
) -> Mapping[str, ValueSchema]:

    invalid = check_valid_field_names(*schema_config.keys())
    if invalid:
        raise Exception(
            f"Can't assemble schema because it contains invalid input field name(s) '{', '.join(invalid)}'. Change the input schema to not contain any of the reserved keywords: {', '.join(INVALID_VALUE_NAMES)}"
        )

    result = {}
    for k, v in schema_config.items():

        if isinstance(v, ValueSchema):
            result[k] = v
        elif isinstance(v, Mapping):
            schema = ValueSchema(**v)
            result[k] = schema
        else:
            if v is None:
                msg = "None"
            else:
                msg = v.__class__
            raise Exception(
                f"Invalid return type '{msg}' for field '{k}' when trying to create schema."
            )

    return result

overlay_constants_and_defaults(schemas, defaults, constants)

Source code in kiara/utils/values.py

def overlay_constants_and_defaults(
    schemas: Mapping[str, ValueSchema],
    defaults: Mapping[str, Any],
    constants: Mapping[str, Any],
):

    for k, v in schemas.items():

        default_value = defaults.get(k, None)
        constant_value = constants.get(k, None)

        # value_to_test = None
        if default_value is not None and constant_value is not None:
            raise Exception(
                f"Module configuration error. Value '{k}' set in both 'constants' and 'defaults', this is not allowed."
            )

        # TODO: perform validation for constants/defaults

        if default_value is not None:
            schemas[k].default = default_value

        if constant_value is not None:
            schemas[k].default = constant_value
            schemas[k].is_constant = True

    input_schemas = {}
    constants = {}
    for k, v in schemas.items():
        if v.is_constant:
            constants[k] = v
        else:
            input_schemas[k] = v

    return input_schemas, constants